Generating and using a location fingerprinting map

ABSTRACT

The present invention discloses, inter alia, a computerized system for building a multisensory location map, the system comprising an interface for receiving multiple multisensory data vectors acquired by multiple mobile devices at multiple locations and accelerometer readings obtained upon movement of at least one device carried by at least one user between the multiple locations; at least a portion of said movement being walking; at least a majority of the multiple locations are located within an area in which an accuracy of global positioning system (GPS) based navigation is below an allowable threshold; and a processor, interconnected with said interface, with accelerometers, a magnetometer and a map calculator, configured for: extracting, out of accelerometer readings, accelerometer information related to multiple walking phases of the walking; for at least two of said multiple walking phases, real-time correcting a currently measured Z vector, and a pitch angle and a roll angle thereof, thereby compensating for horizontal accelerations, thereby obtaining a Z vector pointing toward Earth&#39;s center; calculating, from said Z vector pointing toward earth&#39;s center, a surface parallel to Earth&#39;s face (perpendicular to said Z vector pointing toward earth&#39;s center); estimating, from said surface parallel to Earth&#39;s face and from a magnetic north measured by at least one built-in magnetometer in said at least one device, an offset selected from a group consisting of: an azimuth offset from magnetic north and a heading offset from geometric north; processing the accelerometer information related to said at least two of said multiple walking phases to determine a direction of propagation of the at least one user and correcting said direction of propagation based on said offset; and estimating, from said corrected direction of propagation, at least one location of said at least one user; and the map calculator calculating, in response to the multiple multisensory data vectors and said at least one estimated location, a location fingerprinting map that comprises multiple grid points; each of the multiple grid points comprising a multisensory grid point fingerprint and grid point location information derivable from said at least one estimated location.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Divisional of U.S. application Ser. No. 16/355,826filed Mar. 17, 2019 which is a Continuation of U.S. application Ser. No.15/899,929 (now U.S. Pat. No. 10,278,154) filed Feb. 20, 2018, which isa Divisional of U.S. application Ser. No. 14/355,606 (now U.S. Pat. No.10,111,197) filed May 1, 2014, which is a national phase application ofPCT International Application No. PCT/IL2012/050426 filed Oct. 30, 2012,which claims the benefit of U.S. Provisional Application No. 61/554,556filed dated Nov. 2, 2011, all of which are incorporated herein byreference in their entirety.

BACKGROUND OF THE INVENTION

There is a growing need to provide highly accurate positioninginformation even in indoor spaces or areas in which global positioningsystem (GPS) based navigation is either impossible or can exhibitunaccepted granularity.

SUMMARY OF THE INVENTION

According to an embodiment of the invention a method may be provided andmay include performing any combination of any stages mentioned in thespecification.

Further embodiments of the invention include a computer readable mediumthat is non-transitory and may store instructions for performing theabove-described methods and any steps thereof, including anycombinations of same. For example, the computer readable medium maystore instructions for executing any combination of stages of any methodmentioned in the specification.

Additional embodiments of the invention include a system arranged toexecute any or all of the methods described above, including any stages-and any combinations of same.

A computerized method for building a multisensory location map may beprovided and may include: receiving, by an interface, multiplemultisensory data vectors acquired by multiple mobile devices atmultiple locations and location estimates indicative of the multiplelocations; wherein at least a majority of the multiple locations arelocated within an area in which an accuracy of global positioning system(GPS) based navigation is below an allowable threshold; wherein thelocation estimates are at least partially generated by internalnavigation systems of the multiple mobile devices; and calculating, by amap calculator, in response to the multiple multisensory data vectorsand the location estimates, a location fingerprinting map that mayinclude multiple grid points, wherein each grid point may include amultisensory grid point fingerprint and grid point location information.

The calculating may include correlating, by the map calculator, themultiple multisensory data vectors and the location estimates, toprovide the location fingerprinting map.

The method may include repetitively feeding to a mobile device thatenters the area, location information for fixing location estimategenerated by the internal navigation system of the mobile device.

The method may include updating an accuracy level of grid point locationinformation in response to a number of mobile devices that acquiredmultisensory data vectors in proximity to the grid point.

The method may include updating an accuracy level of grid point locationinformation in response to an acquisition of location specificelectromagnetic signals acquired at a vicinity of the grid point.

The method may include updating an accuracy level of grid point locationinformation in response to global positioning system (GPS) signalsacquired in proximity to the grid point.

The method may include detecting indoor space singularity points andupdating an accuracy level of grid points located in proximity to thesingularity points.

The method may include building a map of the indoor space based upon thecontent of the location fingerprinting map.

The method may include sending to mobile device directional informationin response to a location of the mobile device, a target destination andthe location fingerprinting map.

The location fingerprinting map is a three-dimensional map.

The method may include receiving multiple multisensory data vectorsacquired by a mobile device at a plurality of indoor locations beforereceiving a multisensory data vector at a certain location in which thegranularity of the GPS based navigation is below the threshold, andincreasing an accuracy level of the location estimates of the pluralityof indoor locations based upon GPS location information acquired at thecertain location.

The method may include receiving multiple multisensory data vectorsacquired by a mobile device at a plurality of indoor locations afterreceiving a multisensory data vector at a certain location in which thegranularity of the GPS based navigation is below the threshold, andincreasing an accuracy level of the location estimates of the pluralityof indoor locations based upon GPS location information acquired at thecertain location.

The multisensory data vector may include electromagnetic information.

The multisensory data vector may include at least one out of ambientnoise, temperature, earth magnetic field information.

The multisensory data vector may include pedometer information.

The method may include repetitively estimating acceleration of a userand updating mobile phone coordinate system in response to theestimating.

The method may include repetitively estimating acceleration of a userand updating location estimate generated by the internal navigationsystem of the mobile device in response to the estimating.

There may be provided computerized method for building a multisensorylocation map, the method may include: receiving location information ofa certain location in which a mobile device is located, the locationinformation is acquired by utilizing global positioning system (GPS)based navigation; receiving multiple multisensory data vectors acquiredby the mobile device at multiple locations and location estimatesindicative of the multiple locations; wherein the location estimates aregenerated by an internal navigation system of the mobile device; andassociating an accuracy level to the location estimates based upon anestimated accuracy of the internal navigation system and a distance ofeach location from the certain location.

The accuracy level may decrease with an increase of a distance from thecertain point.

The method may include receiving location information of anotherlocation in which the mobile device is located, the location informationis acquired by utilizing GPS based navigation; wherein the hand-helddevice reaches the other location after reaching the multiple locations;and updating the accuracy level of the location estimates of themultiple points based upon the estimated accuracy of the internalnavigation system and a distance of each location from the otherlocation.

The method may include receiving multiple multisensory data vectors andlocation estimates from another mobile device and updating an accuracylevel to location estimates generated by the other mobile device basedupon the location information acquired by the mobile device.

There may be provided a method for navigating in an indoor space, themethod may include: acquiring by a mobile device global positioningsystem (GPS) location information; acquiring, after stopping acquiringGPS location information, a multisensory data vector and generating, byinternal navigation system, a location estimate of a location in whichthe multisensory data vector was acquired; comparing the acquiredmultisensory data vector to at least one multisensory data vector of atleast one grid point of a location fingerprinting map; determining alocation of the mobile device based upon the comparison; and updatingthe internal navigation system with the location.

The multisensory data vector may include earth magnetic field readingsand communication related electromagnetic signals; wherein thedetermining of the location may include calculating a location estimateof the mobile device based upon a comparison between communicationrelated electromagnetic signals acquired by the mobile device andcommunication related electromagnetic signals of the at least one gridpoint of the location fingerprinting map; and updating the locationestimate based upon a comparison between each magnetic field readingsacquired by the mobile device and magnetic field readings of the atleast one grid point of the location fingerprinting map.

There may be provided a computerized method for building a multisensorylocation map, the method may include: receiving, by an interface,multiple multisensory data vectors acquired by multiple mobile devicesat multiple locations and location estimates indicative of the multiplelocations; wherein at least a majority of the multiple locations arelocated within an area in which an accuracy of global positioning system(GPS) based navigation is below an allowable threshold; wherein thelocation estimates are at least partially obtained by processingaccelerometers readings; and calculating, by a map calculator, inresponse to the multiple multisensory data vectors and the locationestimates, a location fingerprinting map that may include multiple gridpoints, wherein each grid point may include a multisensory grid pointfingerprint and grid point location information.

There may be provided a computerized method for building a multisensorylocation map, the method may include: receiving, by an interface,multiple multisensory data vectors acquired by multiple mobile devicesat multiple locations and accelerometer readings obtained when a usermoved between the multiple locations; wherein at least a majority of themultiple locations are located within an area in which an accuracy ofglobal positioning system (GPS) based navigation is below an allowablethreshold; estimating the position of the multiple locations byprocessing the accelerometers readings; and calculating, by a mapcalculator, in response to the multiple multisensory data vectors andthe location estimates, a location fingerprinting map that may includemultiple grid points, wherein each grid point may include a multisensorygrid point fingerprint and grid point location information.

The method may include extracting, out of accelerometer readings,information relating to multiple walking phases; and processingaccelerometer information relating to two walking phases to determine adirection of propagation of a user.

The method may include detecting maximal accelerometer readings relatedto first and third walking phases out of four walking phases; anddetermining the direction of propagation of the user in response to themaximal accelerometer readings.

The method may include detecting maximal accelerometer readings relatedto second and fourth walking phases and estimating the direction ofpropagation of the user in response to the maximal accelerometerreadings related to each one of the first till fourth walking phases.

There may be provided a non-transitory computer readable medium thatstores instructions for: receiving, by an interface, multiplemultisensory data vectors acquired by multiple mobile devices atmultiple locations and location estimates indicative of the multiplelocations; wherein at least a majority of the multiple locations arelocated within an area in which an accuracy of global positioning system(GPS) based navigation is below an allowable threshold; wherein thelocation estimates are at least partially generated by internalnavigation systems of the multiple mobile devices; and calculating, by amap calculator, in response to the multiple multisensory data vectorsand the location estimates, a location fingerprinting map that mayinclude multiple grid points, wherein each grid point may include amultisensory grid point fingerprint and grid point location information.

The calculating may include correlating, by the map calculator, themultiple multisensory data vectors and the location estimates, toprovide the location fingerprinting map.

The non-transitory computer readable medium may store instructions forrepetitively feeding to a mobile device that enters the area, locationinformation for fixing location estimate generated by the internalnavigation system of the mobile device.

The non-transitory computer readable medium may store instructions forupdating an accuracy level of grid point location information inresponse to a number of mobile devices that acquired multisensory datavectors in proximity to the grid point.

The non-transitory computer readable medium may store instructions forupdating an accuracy level of grid point location information inresponse to an acquisition of location specific electromagnetic signalsacquired at a vicinity of the grid point.

The non-transitory computer readable medium may store instructions forupdating an accuracy level of grid point location information inresponse to global positioning system (GPS) signals acquired inproximity to the grid point.

The non-transitory computer readable medium may store instructions fordetecting indoor space singularity points and updating an accuracy levelof grid points located in proximity to the singularity points.

The non-transitory computer readable medium may store instructions forbuilding a map of the indoor space based upon the content of thelocation fingerprinting map.

The non-transitory computer readable medium may store instructions forsending to mobile device directional information in response to alocation of the mobile device, a target destination and the locationfingerprinting map.

The location fingerprinting map may be a three-dimensional map.

The non-transitory computer readable medium may store instructions forreceiving multiple multisensory data vectors acquired by a mobile deviceat a plurality of indoor locations before receiving a multisensory datavector at a certain location in which the granularity of the GPS basednavigation is below the threshold, and increasing an accuracy level ofthe location estimates of the plurality of indoor locations based uponGPS location information acquired at the certain location.

The non-transitory computer readable medium may store instructions forreceiving multiple multisensory data vectors acquired by a mobile deviceat a plurality of indoor locations after receiving a multisensory datavector at a certain location in which the granularity of the GPS basednavigation is below the threshold, and increasing an accuracy level ofthe location estimates of the plurality of indoor locations based uponGPS location information acquired at the certain location.

The multisensory data vector may include electromagnetic information.

The multisensory data vector may include at least one out of ambientnoise, temperature, earth magnetic field information.

The multisensory data vector may include pedometer information.

The non-transitory computer readable medium may store instructions forrepetitively estimating acceleration of a user and updating mobile phonecoordinate system in response to the estimating.

The non-transitory computer readable medium may store instructions forrepetitively estimating acceleration of a user and updating locationestimate generated by the internal navigation system of the mobiledevice in response to the estimating.

There may be provided a non-transitory computer readable medium thatstores instructions for: receiving location information of a certainlocation in which a mobile device is located, the location informationis acquired by utilizing global positioning system (GPS) basednavigation; receiving multiple multisensory data vectors acquired by themobile device at multiple locations and location estimates indicative ofthe multiple locations; wherein the location estimates are generated byan internal navigation system of the mobile device; and associating anaccuracy level to the location estimates based upon an estimatedaccuracy of the internal navigation system and a distance of eachlocation from the certain location.

There may be provided a non-transitory computer readable medium thatstores instructions for: acquiring by a mobile device global positioningsystem (GPS) location information; acquiring, after stopping acquiringGPS location information, a multisensory data vector and generating, byinternal navigation system, a location estimate of a location in whichthe multisensory data vector was acquired; comparing the acquiredmultisensory data vector to at least one multisensory data vector of atleast one grid point of a location fingerprinting map; determining alocation of the mobile device based upon the comparison; and updatingthe internal navigation system with the location.

There may be provided a non-transitory computer readable medium thatstores instructions for: receiving, by an interface, multiplemultisensory data vectors acquired by multiple mobile devices atmultiple locations and location estimates indicative of the multiplelocations; wherein at least a majority of the multiple locations arelocated within an area in which an accuracy of global positioning system(GPS) based navigation is below an allowable threshold; wherein thelocation estimates are at least partially obtained by processingaccelerometers readings; and calculating, by a map calculator, inresponse to the multiple multisensory data vectors and the locationestimates, a location fingerprinting map that may include multiple gridpoints, wherein each grid point may include a multisensory grid pointfingerprint and grid point location information.

There may be provided a non-transitory computer readable medium thatstores instructions for: receiving, by an interface, multiplemultisensory data vectors acquired by multiple mobile devices atmultiple locations and accelerometer readings obtained when a user movedbetween the multiple locations; wherein at least a majority of themultiple locations are located within an area in which an accuracy ofglobal positioning system (GPS) based navigation is below an allowablethreshold; estimating the position of the multiple locations byprocessing the accelerometers readings; and calculating, by a mapcalculator, in response to the multiple multisensory data vectors andthe location estimates, a location fingerprinting map that may includemultiple grid points, wherein each grid point may include a multisensorygrid point fingerprint and grid point location information.

The non-transitory computer readable medium, wherein the accuracy leveldecreases with an increase of a distance from the certain point.

The non-transitory computer readable medium may store instructions forreceiving location information of another location in which the mobiledevice is located, the location information is acquired by utilizing GPSbased navigation; wherein the hand-held device reaches the otherlocation after reaching the multiple locations; and updating theaccuracy level of the location estimates of the multiple points basedupon the estimated accuracy of the internal navigation system and adistance of each location from the other location.

The non-transitory computer readable medium may store instructions forreceiving multiple multisensory data vectors and location estimates fromanother mobile device and updating an accuracy level to locationestimates generated by the other mobile device based upon the locationinformation acquired by the mobile device.

The non-transitory computer readable medium, wherein the multisensorydata vector comprises earth magnetic field readings and communicationrelated electromagnetic signals; wherein the determining of the locationcomprises calculating a location estimate of the mobile device basedupon a comparison between communication related electromagnetic signalsacquired by the mobile device and communication related electromagneticsignals of the at least one grid point of the location fingerprintingmap; and updating the location estimate based upon a comparison betweeneach magnetic field readings acquired by the mobile device and magneticfield readings of the at least one grid point of the locationfingerprinting map.

The non-transitory computer readable medium may store instructions forextracting, out of accelerometer readings, information relating tomultiple walking phases; and processing accelerometer informationrelating to two walking phases to determine a direction of propagationof a user.

The non-transitory computer readable medium according to claim 33 thatstores instructions for detecting maximal accelerometer readings relatedto first and third walking phases out of four walking phases; anddetermining the direction of propagation of the user in response to themaximal accelerometer readings.

The non-transitory computer readable medium may store instructions fordetecting maximal accelerometer readings related to second and fourthwalking phases and estimating the direction of propagation of the userin response to the maximal accelerometer readings related to each one ofthe first till fourth walking phases.

BRIEF DESCRIPTION OF THE INVENTION

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings in which:

FIG. 1-12 illustrates an indoor space and multiple grid points accordingto various embodiments of the invention;

FIGS. 13-18 illustrates various screens shots that are displayed to auser of a mobile device a prior art read threshold voltage distribution;

FIGS. 19-21 illustrates accelerometer readings, different phases ofwalking of a person and an evaluation of a direction of propagation of auser according to various embodiments of the invention;

FIGS. 22-23 illustrate various forces and coordinate systems accordingto various embodiments of the invention;

FIGS. 24-28 illustrate various methods according to various embodimentsof the invention; and

FIG. 29 illustrates a system according to an embodiment of theinvention.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

Because the illustrated embodiments of the present invention may for themost part, be implemented using electronic components and circuits knownto those skilled in the art, details will not be explained in anygreater extent than that considered necessary as illustrated above, forthe understanding and appreciation of the underlying concepts of thepresent invention and in order not to obfuscate or distract from theteachings of the present invention.

Any reference in the specification to a method should be applied mutatismutandis to a system capable of executing the method and should beapplied mutatis mutandis to a non-transitory computer readable mediumthat stores instructions that once executed by a computer result in theexecution of the method.

Any reference in the specification to a system should be applied mutatismutandis to a method that can be executed by the system and should beapplied mutatis mutandis to a non-transitory computer readable mediumthat stores instructions that can be executed by the system.

Any reference in the specification to a non-transitory computer readablemedium should be applied mutatis mutandis to a system capable ofexecuting the instructions stored in the non-transitory computerreadable medium and should be applied mutatis mutandis to method thatcan be executed by a computer that reads the instructions stored in thenon-transitory computer readable medium.

A system and a method for indoor micro positioning and indoor navigationusing handheld devices and smartphones are hereby described. Modernsmartphones are equipped with many sensors and sources of information.Accelerometers, gyroscopes and digital compasses enable inertialnavigation.

Reception of various signals (e.g. GPS, Aided-GPS (AGPS), GSM, WLANAccess Points (WAP), BlueTooth (BT), InfraRed (IR), CDMA, 3G, 4G/LTE,NFC, Cellular, FM Radio, TV, Earth Magnetic Field Size and Direction,WiMax etc.) can be sampled to create location fingerprinting that areunique per each given position indoor or outdoor.

In recent years GPS is embedded in most smartphones and handhelddevices. The advantage of outdoor positioning using the GPS isestablished and multiple useful commercial applications exist.

The System and Method may rely on Wi-Fi, GSM, AGPS and other SensorsLocation Fingerprinting. Radio map or location fingerprinting map is apredefined infrastructure. It can be described as a grid of points thatcovers an indoor (or outdoor) space like a mall floor plan. Roughlyevery few meters (1 to 5) not necessarily aligned. For each grid point(a specific location) a measurement was performed and multiple sensordata was collected.

If for example an indoor area is covered with Grid Points (GP) every 1-5meters, a comprehensive measurement per a point will include the vector(or group) of values of each and every received signal (e.g. WiFi,GPS/A-GPS, GSM etc.)

For every GP, such vector of up to dozens of values may include theunique identity of every received WAP (i.e. specific WAP MAC ADDRESS orphysical address of the router) and WAP Received Signal StrengthIndication (RSSI).

Currently not all mobile operating system incorporates access tohardware signals. Android based device enables that. As for IOS6(iPhone's operating system), BlackBerry OS and WP7 (Windows Phone 8) andrelated devices the details will be clearer soon enabling applicationfor as many mobile devices as possible.

Furthermore, the signal strength from as many as possible (up to 40)cellular towers, as part of the multisensory location fingerprintingdata, are stored even if the reception is too weak for Cellularcommunication. Additional signals as may be available such as Televisionbroadcast, GSM, CDMA, 3G, FM radio reception, WiMax and additionalsignals may be added.

Multisensory Location Fingerprinting

A multisensory location fingerprinting data (also referred to as Radiomap) in the context of this described method and system, is not limitedhowever to registering electromagnetic signals. Additional cues asmicrophone, ambient light, temperature, barometric pressure reading etc.are considered for diversifying the strength of every single grid pointlocation typical signature, thus called location fingerprinting.

One key criterion for deciding whether or not to incorporate a signal(existing or futuristic) in the specific location signature is itslocation specific nature. Specific indoor location may be thus sketchedwith great details.

Creating location fingerprinting map involves huge, labor intensiveeffort in terms of resources, time and cost since calibration requiresenormous number of measurements of full vectors of measurement results,along a minimal time for each grid point.

In addition calibration, once made is subject to timely updates and isnot long lasting (additional access point, removed or shifted Bluetoothtransmitter, new cellular tower etc.) making re calibration for manysignificant indoor areas impractical or commercial inacceptable.

The described system and method harness crowd of users to deploy andmaintain such location fingerprinting anywhere.

Inertial Navigation Systems (INS)

Inertial navigation is an established method used in multiple platforms(e.g. aircrafts, ships, missiles etc.) for relative navigation. Inertialnavigation loses accuracy over time. Main factors to dictate how fastinertial navigation will turn useless for indoor navigation include thequality and physical capabilities of the sensors. The main advantage ofthe inertial navigation system is its total independence.

The noisier the accelerometer, gyroscopes and compass (the compass isnot mandatory for inertial navigation but exists in most current andfuture devices) the less time one can rely on pure inertial navigationonly for positioning. In addition, small commercial accelerometers andgyroscopes that are embedded in handheld devices these days are not asaccurate as those of an airplane or a ship.

Inertial navigation system can only measure the displacement of thedevice, in six degrees of freedom (3 degrees of freedom for position and3 angles to dictate orientation) from its initial position andorientation as oppose to its absolute location. This makes the accuracyof inertial navigation tightly related to the accuracy of the initialposition and the elapsed time since inertial measurements begun.

The trend of including accelerometer, gyroscopes and digital compassesto smartphones drives this niche electronic market to new peaks.Smaller, cheaper more capable inertial sensors appear with betteraccuracy and improved Signal to Noise Ratio (SNR). For the first time 9degrees of freedom sensors are embedded in the iPhone 4, 4s, 5. Androidbased devices such as Samsung S2, S3 and Galaxy Nexus are coping withthe new standard.

3D accelerometer enables the extraction of the displacement in 3 axesusing double mathematical integration (acceleration to velocity changeand velocity change to position displacement) and once added to originalposition the new position is computed. 3D gyroscopes sense the angularvelocities of the device enabling the computation of device actualPitch, Roll and Yaw as function of its initial orientation. 3D digitalcompasses measure the direction of the geomagnetic field. That enablesthe computation of the device's absolute orientation.

Each of the 3 sensor types can somehow compensate for the other sensorsinherent disadvantages. The compass is relatively stable, but slow inreaction and susceptible to local interference (metal objects, localmagnetic fields etc.) Its reading can be improved by the accelerometerand or gyroscope (that reacts faster but is noisier)

All these improvements in the modern mobile's inertial sensors, drivenby strong and sustainable market need are subject to physicallimitations. State of the art heavy and expensive inertial navigationsystems (INS) are subject to drifts of hundreds of meters per hour asminimum. In case that no fix points are available to update or fuse intothe INS it soon becomes useless for indoor navigation.

The described method and system uses periodical fix updates to renewmobile inertial navigation positioning.

Inertial Navigation System Fix Update

One method used for contributing to the inertial navigation accuracy isinjection of positioning data from other sources. Modern high-endinertial navigation systems use the described methodology to improveinertial navigation over time. Positioning from another source (usuallyGPS for outdoors) is measured continuously and injected or fusesperiodically to the inertial navigation system to fix it.

As absolute positioning from the GPS for instance is considered moreaccurate, system can still get the benefits of the inertial system (thatcan position and navigate without GPS) and use the fix points toeliminate the aggregated error. Thus, every few seconds the inertialnavigation system gets a fresh start with accuracies that are goodenough for positioning and has more time of good positioning estimation.

Eliminating the drifts of the inertial system by fix points from anothersource is an established way to get the most out of inertial navigationsystems.

Dead Reckoning (DR) is Used to Improve Positioning

Dead Reckoning is equivalent in nature to the inertial navigation systemapproach. The process used to estimate the position of an objectrelative to an initial position, by calculating the current positionfrom the estimated velocity, travel time and direction course. Moderninertial navigation systems depend on DR in many applications.

The described system and method uses built-in mobile accelerometers todetect human steps and identifies the step length based on biomechanicalcharacteristic of the step. The type of step can depend on differentfactors such as gender, age, height and weight of the person. Measuringstep size, step direction and counting identified steps can produceadditional solution for how big the displacement is.

Map Matching

Map Matching is a method for merging data from signal positioning and agiven digital map network to estimate the location of the mobile objectthat best matches the digital map. The reason that such techniques arenecessary is that the location acquired from positioning techniques issubject to errors. Map matching is often helpful when the position isexpected to be on a certain path, such as in the problem of tracking amoving vehicle on the route of GPS device. When indoor we can usuallyassume flat floors and straight hallways. This way we can eliminate(reduce to zero) erroneous readings from the inertial navigation systemaround Z axis and same for X, Y axes as well as angular accumulatederrors (from gyroscopes or digital compass).

Please refer to Initial Use paragraph for further elaboration on thebasic maps that are manually or semi automatically prepared or achievedvia a collaboration with industry leaders in mapping indoors (Google,Microsoft and few startup companies) or with the indoor representatives(mall management etc.)

The described system and method can work with basic maps or no maps atall and yet produce enhanced indoor positioning and navigation in ‘new’indoor areas since first use.

There are two forms of map matching algorithms: online and offline. Theonline map matching algorithm deals with situations in which onlycurrent and past data available to estimate the position indoor andimprove positioning in new places that have not yet been surveyed byusers. The offline map-matching algorithm is used when there is some orall future data is available (the recorded GP's) so the absoluteposition of the GP's is fine tuned to match more accurately the map.

The suggested system and method is unique in creating a cyclic processof improvement. Users get fair indoor accuracy using current positioningtechniques, improved with described algorithms (including real time mapmatching, in case that basic map was made or given—which is NOTmandatory). They record grid point (location fingerprinting readouts ofrelevant onboard mobile sensors). The grid points serve other users (atlater time) by enabling better positioning on the inner indoor parts(even before the grid is full), eliminating aggregated errors andcreating better maps. Better maps use offline map matching to improve GPpositions, and the cycle goes on bringing more accurate positioning andnavigation and more accurate mapping infrastructure for the users.

Additional Built-in Positioning and Navigation Aids

While out of GPS sufficient reception (mainly indoor but not only), GPSmay become inaccurate for positioning but is still valuable locationfingerprinting collection. The indoor GPS signals, though weak, arestored in a raw format as part of the vector data for each location.

If for example we receive satellite ID and its signal, that signal andits metadata, while many times worthless for positioning will be part ofevery unique multisensory data vector that is measured by crowd ofusers, transmitted and stored in DB.

Even while out of GPS sufficient reception, few sensors can supportpositioning. Cellular reception is used and yields 60-300 m accuracy(based on Google Maps observations indoors and some literature). Ourapproach will take advantage of all the Cell towers in range of allavailable operators. We will not limit the algorithm in using onlyspecific (or a subset) of Cell towers of the chosen operator or thestrongest ones but all. In addition, pico cells and femto cells (smalllocal cellular antennas) that are installed in small places to handlefew cellular devices can enhance cellular location and produce uniquelocation fingerprinting. Both effects will be taken advantage of toimprove indoor positioning.

Detailed System and Method Description

The system and method described hereby are aimed at taking the advantageof the crowd to create an ongoing calibration process. Namely, people(or the crowd) that use the suggested application are walking (ordriving) around and inside places of interest in which none or poor GPSreception exists thus contributing to the process.

In FIGS. 1-8 grid points GP1-5 are denoted 10, 20, 30, 40 and 50respectively, dashed arrows 11, 12, 13, 14 and 15 illustrates the pathbetween two consecutive grid points of grid points 10, 20, 30, 40 and 50respectively. FIG. 5-8 also illustrate another grid point 10′ and a path12′ traversed by another user (or by the same user) at later time. Theindoor space is denoted 100.

FIG. 1 depicts an indoor area 100 without (or with insufficient) GPSreception for accurate positioning (the described system and method doesuse even residual GPS/AGPS reception as part of its locationfingerprinting unique signature). A pedestrian (user) carries the mobiledevice while application is active. At this stage, no prior data isknown (in the system database) about the internal floor plan, not even abasic map (floor plan). While outside he gets valid GPS reception usedin conjunction with inertial, Cellular (GSM, CMDA, 3G and otherstandards) navigation. This reception fixes the system and eliminatesall accumulated drift errors and creates an excellent baseline towardslosing the GPS signal. While walking, Grid Points (GP) are recorded withmultisensory data vectors that create unique location fingerprinting foreach GP. GP1 10 and GP2 20 are recorded and sent over the web connectionto the grid database on the remote server. Crossing the entrance (GP220), last GPS update (also referred to as Fix) lays good baseline forinitial position for inertial navigation.

FIG. 2 depicts the record of GP3 30. That Grid Point 30 is new and itgraded high (marked as small circle) for its absolute position based onthe very good accuracy of the inertial navigation system at this stage(along arrow 13)—few seconds after the last GPS fix (the inertialnavigation is totally independent and thus accurate and available indoorfor short time) together with Cellular triangulation (and additionalestablished multi operator and multi Cell towers based) are fused usingstandard prediction algorithms (such as Kalman Filter, Extended KalmanFilter, Reduced Particle Filter or equivalent statistical method) tomaintain good accuracy.

FIG. 3 depicts the growing drift (marked medium now—arrow 14) since lastaccurate fix point (at indoor area entrance GP2 20) so small differencesand uncertainties are accumulated. GP4 40 thereby recorded fully (allrelevant multi sensor location fingerprinting). The time intervalbetween PG3 30 and GP4 40 represents a parameter that will vary from 1-5meter. Producing higher resolution grid (with many adjacent grid points)will later support better positioning. The grade for GP4 40 at thisstage is lower than GP3 30 because we know its absolute position butwith less certainty and thus GP4 40 is marked as medium circle.

FIG. 4 depicts the man with mobile device moving further inside theindoor area. It is important to mention that the amount of points chosento demonstrate the rate in which overall accuracy is reduced is just forsimplicity and ease of presentation. It is possible that modernsmartphones will have better inertial navigation performance, especiallywhen aided with indoor Cell reception and positioning, dead reckoningand map matching algorithms. GP5 50 is created with relatively low grade(because drift along arrow 15 is high and is accordingly marked as bigcircle), a multi sensor fingerprinting data vector is recorded (likealways when application is running) GPs in general are subject forongoing improvement in their absolute location based on gathered datafrom crowd and updated measurement as for the data vector of theirlocation fingerprinting. It is possible that at a certain stage theaccuracy of the indoor positioning for the first user that entered theindoor area will drop under acceptable accuracy for good user experiencein indoor positioning.

FIG. 5 depicts a second, another or the same individual walking insidethat same indoor area at a later time. Please be aware that thispresentation is simplified as people may enter and exit from multipleplaces thus contributing gradually to the rate, update, robustness andaccuracy of the grid. This user is taking a different path when outdoor(contributing another GPS based GP marked 10′ with full data recorded tothe evolving database on the server side) but enter through the sameentrance (near GP2 20). While walking inside, his device experiencesminor drift near or along arrow 13. While nearby GP3 30 his mobiledevice records a point and compares its data vector against the existingGPs in real time. Please be aware that user relies for the first time onthe GPs that were collected to improve his indoor accuracy in real time.Using statistical methods (given distribution function, typical changesover time and different mobile devices performance in reception qualityas well as other factors) a fix occurs (We assumed here, again forsimplicity, that he took a very similar indoor path but even if not,another valid point was collected near GP3 30). That fix eliminates allhis device's inertial navigation accumulated errors.

FIG. 6 depicts the user going further inside with minor drift (along ornear arrow 14 as oppose to medium drift the first user had along arrow14) again (like the first user had along arrow 13) and when comparedagainst GP4 40 the grade of GP4 40 increases (now marked by small circleas oppose to medium circle before) since we now know its absoluteposition with better accuracy. In case that the user stopped for a fewseconds (no steps monitored and velocity in x, y and z below minimalthreshold logic) a longer sample is taken and such grid point accuracyrank for location fingerprinting measurement (as oppose for absoluteaccuracy) is increased. Longer static measurement produces valuable dataas for the changes or fluctuations over time of the locationfingerprinting vector data. Each GP has a grade for its absoluteaccuracy (based on the size that will be described in the followingparagraphs) and a grade for the measurement length (given that user isstatic). Our analysis demonstrates that ˜20 seconds is good to upgradeor restore the location fingerprinting sampling grade to maximum. Longermeasurement can also assist the algorithm in coping with thedisappearance and reappearance of certain access points readings thatmay occur timely.

FIG. 7 depicts the same individual (again choosing very similar actualpath for simplicity) moves further inside with only medium drift near oralong arrow 15 (as opposed to high drift rate for the first user becausehe had no fix for longer time). Algorithm is than able to upgrade GP5 50from low accuracy (big circle) to medium accuracy (medium circle). Thesize of the GPs is chosen to emphasize its grade in terms of itsabsolute location. Small is for accurate absolute location, high gradeGP, medium represent mediocre uncertainty per GPs absolute location (asa result of more significant drift) and big represents biggeruncertainty and lower grade in terms of absolute position of thecorresponding GP. Collected data include the mobile type (registeredonce and updated automatically as needed) to cope for differences inreception levels with different mobile devices.

FIG. 8 depicts the grid after further improvement (GP5 50 is marked assmall circle to represent high accuracy in its absolute position) afteradditional users with active application on their mobile device hadsurveyed the same indoor site (for example based on minor drift near oralong arrow 15 as a result of fix near GP4 40). The ongoing Wi-Fi,Cellular (and additional multi sensor) location fingerprinting is crowdsourced. The process in turn enables better accuracies for indoorpositioning.

In FIGS. 9-11 multiple GPs are shown. Multiple grid points of highaccuracy are denoted 90 and marked as small circles, medium accuracygrid point is denoted 91 and low accuracy grid point is denoted 92.These grid points are collected in different times possibly by differentusers. It is important to remember that many visitors walking differentpaths each from and to other stories, elevators, escalators, exits andentrances using different devices and measuring new GPs in differentorientation (e.g. handheld, in pocket, in the car while parking indooretc.) contribute to accelerate the formation of the crowd sourced multisensor location based fingerprinting map.

In that case, a user (potentially a ‘regular’ user as oppose to earlyadaptor or mapper as described before) initiated his application whileindoor having no initial GPS fix and arrow 99 illustrate an initialestimate of an initial path traversed by the user. Using best availableCell positioning and inertial displacement (together with DR—using stepidentification module) multisensory location fingerprinting vectors arerecorded and compared against the existing location fingerprinting orgrid database (that is managed on the server but is loaded dynamicallyto the device.)

FIG. 10 depicts a user fixing 98 his position indoor (madeautomatically) enabling him to initiate rather accurate indoornavigation (as one must know his own position in order to create a fullreal navigation solution). He then passes in vicinity to another GP andanother fix to the corresponding closest GP will happen automatically.Although created by few users, that grid of multisensory locationfingerprinting (also referred along this document to as Radio map) isimperfect. It can be seen by the existence of Medium and big GPs thatmark accordingly lower grade and larger uncertainty for them in terms oftheir absolute location. That may occur dynamically if for example a newaccess point was added or a Cell tower was removed. Although notespecially susceptible for such changes, due to wide base ofmultisensory data location fingerprinting collected, the algorithm mayreduce the grade of a GP based on updated data (or missing data asdescribed before). Please notice that if the individual that carries hismobile will make a random 90 degrees right turn, the algorithm willautomatically upgrade the Medium GP 91 to a Small one (because a userwith minor drift and very recent highly accurate fix is approaching it)and later the big GP 92 to an Medium one.

FIG. 11 depicts an established updated grid of multisensory locationfingerprinting with a mobile device that is assumed to be near GP 92that is marked as big circle for its low accuracy in terms of itsabsolute position. The current positioning in real time is mediocre andmay even be insufficient for good user experience for indoor positioningand navigation. The user goes outside the indoor area 100 (arrow 97) topoint 93. Point 93 may be measured for the first time but since it isoutside area 100, GPS reception is regained and thus GP 93 with highaccuracy is created. The indoor area 100 can be exited through an exit(it is unknown to the algorithm and irrelevant to define whether or notthe exit is wide or narrow or the exit exact location at this stage).

FIG. 12 depicts backtrack rank upgrade (dashed arrow 96) for the big GP92 and due to its proximity to grid point 93 the accuracy of grid point92 is now higher. That is done offline by the algorithm taking advantageof the rather accurate, non-GPS based estimation of the last few indoorlegs or sections. For example, if magnetic North is up, we know (basedon inertial navigation) that he was walking 23 meters in the direction317 degrees from the big GP 92 to the Small GP 93 (GPS reception renewaland fix point) GP. Thus the grade of the last indoor point can becomeMedium or even Small (depending on the time and overall accuracy sinceprevious indoor fix). Former GPs ranks can be upgraded in a similarmanner based on the drift that is growing backwards when we look at itfrom the exit back to the indoor area. This way each unique GP rank canbe upgraded (both forward in real time and backward and forward offline)and the grid is improved for the benefit of all current and futureusers. It is noted that the improvement is not limited to the directionor path of the last user or any user, as we know the offset betweenadjacent GPs. Once a GP was upgraded in terms of its absolute position,it may upgrade adjacent GPs accordingly by reducing their absoluteposition inaccuracies. Unique indoor points may refer to, but notlimited to, Elevators, escalators, stairs, sudden GPS reception indooror even a manual Mark made by the user. Mark means that the userinteracts through the user interface to add a known point, like restroomposition, business position etc. The algorithm is designed to send theuser push notifications once defined that he is standing, leaving car(using on accelerometer-based steps identification module), enteringElevator, approaching exit, staircase etc.

It is noted that the method uses crowd collected location fingerprintings. The described system and method is not aimed at building adatabase of the actual or proximate base stations (a name for Cellulartowers or Wi-Fi access points, Bluetooth routers etc.) absoluteposition, strength floor plan exact architecture, physical propagationetc. (also known as modeling or simulation approach) but rather torecord wide data vectors for multiple sensors for each grid point. Thismethod enables better indoor accuracy that is sufficient for getting toa room or a store, locate points of interest etc.

One main disadvantage of the modeling or simulation approach, namelycollecting vast data in advance on base station absolute position, exactfloor plan architecture etc. is the imperfect application of physicalpropagation model in complex indoor arenas. In addition, complexphysical effects as multipath etc. makes simulation of signal strengthdifficult. That's in turn causes insufficient indoor positioningaccuracies of 20 meters or more. To improve that a very detailed model(as opposed to a basic map or floor plan) of indoor walls, corners, 3Darchitecture, materials etc. is required making it less practical.

Continuous ‘social’ usage of the hereby described method and system andapplication on a critical mass of mobile devices will leverage theirsurveying to create a live, up to date multi sensor locationfingerprinting infrastructure or grid points database.

The grid (also referred to here as Radio map or calibration grid) willgradually expand to cover (in a viral-like manner) more and more indoorspaces of interest. This grid will continuously improve indoorpositioning supporting full 3D indoor navigation experience.

Collected paths will, in turn, be used to create and (refine existing)3D maps of indoor places of interest. Accurate 3D maps will be published(in a controlled manner) for the benefit of all users. More and moreindoor areas will enrich the database on daily basis.

All this can be achieved based on no prior data, no additional hardware,and no initial calibration. In addition, no complicated business modelsare required (like negotiating on mall-by-mall basis to get updated map,access points exact 3D positions, or cooperation in other issues). Thatin turn will support mapping of distinct and regular buildings andindoor areas around the world rapidly and efficiently.

FIG. 13 depicts recorded collection of many possible paths 110 actuallymade by users over time. The system continuously record, every timeinterval (optimized also for extended battery life, mobileinfrastructure Client Server bottlenecks and additional establisheddesign and system architecture criterion) the location fingerprinting,transmit it over the web and saves it for future use. In real time itmatches the multisensory location unique data vector to the currentadjacent GPs 90 thus enabling indoor positioning to the maximumavailable accuracy. The system also records anonymously and stores onthe database all (or part of) users paths. Unified view of many actualpaths that are based on enough reliable GPs 90 is a valuableinfrastructure for itself and a powerful enabler for numerousapplications. Even a simple layer of all recorded path on Google maps orMicrosoft Bing Mall Maps (an initiative to map indoor areas fornavigation but with no positioning solution) enables using automated orsemi-automated tools for the production of skeleton map. As altitude istaken into consideration (for instance for identifying floor change,while on escalator) those maps are created in 3 dimensions. Thedirection in which stairs are approached supplies vital clue as forclimbing or descending to the adjacent floor. Time in elevator withknown velocity can contribute also for the floor identification.

FIG. 14 depicts linear lines (may be referred to as fences or hedges)around and not cutting in general the unified actual paths map. Users(whether visitors, business employees, business owners, campus studentsetc.) can Mark business 120 (and also rooms, stairs, hallways, gates,baggage claim, passport control, parking entrance etc.) via the mobiledevice interface and upload pictures of certain places indoor or out.That can be achieved by using commercial of the shelf tools for indoordesign for places of interest. Algorithm may also seek for unique Wi-Finames (e.g. “Gap Free Access”) and cross that with existing databasesabout the place we are in (we easily know that using Cellularpositioning). We can assume that floors are flat and hallways walls arestraight for simplifying map production. Automated algorithm thatidentifies areas in which individuals were walking or driving while GPSreception is good, can mark these parts as open parking lot thuscontributing to the accuracy and shape of the contour lines of theindoor space. In addition, based on applications like Google streetview, Google maps 6.0 (that includes an initiative for indoor mapping)and Google Earth exits and entrances (from the street) to indoor areascan be identified and contribute unique points to enable tighter mapmatching around these points. Once produced, map can be duplicated toadjacent floors as baseline, subject of course to community members orback office updates and editions. Map creation is not limited tohallways and can map the inside of big stores likes Sears, Macy's,Nordstrom, Wal-Mart etc. of course no cooperation is required with thestore to achieve that.

FIG. 15 depicts real time (or post action analysis primarily availablefor back office use and or premium users subject to all applicablelimitations as for privacy issues). Community related info, tips,coupons, data etc. and any other location based pieces of informationthat are of commercial or social value to users are shared. Numerousapplications can be based on the created infrastructure. The faces 130mark the position in real time of friends and family that the user maywant to find or locate indoor. Subject to the friends (taken from theusers Facebook or equivalent account for example through simple UI)privacy settings they will appear on the device screen possibly viaaugmented reality (AR) interface.

FIG. 16 depicts powerful business intelligence, consumer behavior andanalytics tool. Real time heat map for additional what or where is ‘hot’now? what is going on near me right now? sort of applications. It can bealso of extreme value for retroactive analysis by different experts. Themap of the floor 120 can be augmented by items 140 (for example ofdifferent color) that indicate the amount of traffic that passes throughdifferent locations of the floor per time. In addition, further analysiswill reveal typical paths of users along the indoor venue, all subjectto acceptable user privacy concerns.

The system and method described hereby starts from no prior data reindoor areas, leverage the crowd sourcing phenomena to defuse a grid ornet of grid points (GP) from the outside towards the inner depth ofrailway stations, hotels, museums etc. while featuring acceptable userexperience in terms of positioning accuracy (10-20 meters initially)upon first entry to a new building (that does not exist on the databaseyet) an infrastructure is deployed and getting improved. That in turnsupports unprecedented (without additional hardware) accuracies of 5-7meters (room level accuracy) or better for later (once critical mass ofGPs are collected by active users surveying that indoor arena) indoorpositioning and indoor navigation.

The created infrastructure will serve as a base for multiple veryattractive commercial, local commerce, loyalty related, consumersatisfaction, customer service and social applications. Triggers forretail will enable deal-based coupon and shopper marketing at the actualpoint of sale or in vicinity to the aisle, shelf or product. Search ofshops or products will enable income through sponsored links mechanism.The created database may be referred to as indoor positioning system(IPS) or indoor GPS is proprietary and thus may be commercialized tothird parties as software development kit (SDK) or API.

FIG. 17 depicts schematic view of the optimal path 150 for user 151 fordrawing cash from nearest active ATM 155 (out of order ATM will bemarked as inactive), buying something 152 (in a shop within openinghours as we know when that from the database when we get there) andgetting back to the car in an indoor parking lot and driving away 153.Additional cool applications may combine Cellular wallet facilitatingmicro indoor positioning accuracies. Other applications may improvecustomer service and overall purchase experience. An example may be tocall the attendant for help while in front of the shelf (assuming thesales representative too is an active user with the describedapplication running on his web-enabled handheld mobile device.)

FIG. 18 depicts possible disaster management related scenario in which areport initiated by a user 15 (and probably controlled by centralalgorithm as for example for the number of different users reporting thesame event at limited time frame and are actually in near vicinity etc.)Fire 122, fire extinguishers, water hose 123 and emergency exits (whichwere maybe marked in advance by municipal fire departments on athird-party application) and elevators 121 may appear or flash. Similaruses can guide visitors out of a hotel or museum, get them to an Airportgate on time based on their actual location or to a working (as opposeto malfunctioned one maybe) defibrillator for performing quickresuscitation in a patient. The application provides the best escapepath 150.

Singular Indoor Points and their Contribution to Faster Grid Deployment

Singular points are narrow places that may be used as good fix points.Once the algorithm identifies that a user just exited to the street andgot a GPS fix, his last inertial based collected grid points gets higherrank. The reason for that is that we now know exactly where he is, butwe also know the exact 3D displacement for the last 10-60 seconds(depending on the grid points deployment and the time since last fix).Thus, we can ‘go back’ and increase, in reverse order, the grade (interms of absolute indoor location) for the last grid points hecollected. Same goes for entering an elevator, stating the escalator, orexiting the stairs peer.

This paragraph describes a method to faster deploy the grid point. Itfacilitates not only the entrance (which represents the last GPSsignal). Many indoor places enable us to increase the grade of acollected grid point. It can be in real time, while moving forward andgetting to a place near a wide window and suddenly get GPS receptionindoor.

It can also work offline, when the algorithms identifies that the lastcollected grid points (by the individual that just stood by the windowand got GPS reception and thus a very accurate fix) are more valuablethan the poor grade they just got (because they were inaccurate due totoo long time since last update) but they are upgraded from the GPS fix.

Required Accuracy

Indoor positioning of 5-7 meters or better is sufficient for good indoorpositioning experience. On one hand 10-20 meters may lead to annoyingmisleading navigation commands, and sub-meter accuracy is not required(and most probably impossible with software only based solution) to getto restrooms, room, shop (even small one), exit, ATM, hotel room, carparking lot etc.

Leveraging Dynamic Base Stations to Improve Indoor Positioning

As robust collection of multisensory location fingerprinting involveswide range of existing and future sensors dynamic networks areconsidered too. Bluetooth devices and Wi-Fi hotspots exists on manyhandheld and modern smartphones. Part of the collected data vector willbe allocated for networks that are mobile by nature.

Provisions of dynamic wireless communication base stations includingWireless LAN communication or comparable standards in vehicles areexplored (also known as v2v for vehicle to vehicle wirelesscommunication.) Trains and buses already equipped in few countries withWi-Fi access points. Those dynamic inputs may be static for certaintimes (parking car or standing or sitting man with laptop) and improverobustness and accuracy to indoor positioning.

The described system and method utilizes such temporal dynamic Wi-Fi,Bluetooth and emerging base stations technology to improve locationfingerprinting and indoor positioning.

In case that a user is in the same floor with another user at withinwireless communication range, we can assume open space propagation model(as for the function that computes the decrease in signal per distance)and triangulate one or more to enhance positioning. We can assume openspace based on the floor plan when given. We know that distinct usersare in close vicinity by directly comparing their vectors of locationfingerprinting (taking into consideration the known differences indevices hardware e.g. antenna strength etc.) that is powerful because wedo not necessarily have to know where they are exactly but know thatthey are close, static, moving together (continuous locationfingerprinting similarity) or not. Such co-movement or temporal huddlingcan serve for many applications that are related to events, dating,business situations, shopping preferences etc.

If for example a man stands outside the mall with fair PGS reception andanother user sits in a Coffee place indoor, they can both contributesignificantly to the position accuracy in real time of a third userwithin reception range from both. This improvement may prove beneficialmostly for ad-hoc cases, but as many users are indoor, it may contributeto the position and fasten the structure process or (a diffusion-likeprocess as described) of grid points sampling (with multisensory data)and validation. If we know (for example from the floor plan) that thereis an open space from the man in the coffee (indoor well positioned) tothe third user (indoor but with mediocre positioning solution) andbetween the man outside (with GPS reception and excellent positioning)and the third user, we can significantly improve third user positioning.

System Architecture Considerations

The described system and method is designed as a client serverdistributed architecture. Databases of grid points and maps are storedin the server. Server side involves described algorithm and some COTSmap editing tools. Dynamic subset of Databases of grid points may bepushed automatically in real time to users devices so that relevant userproximity can be analyzed and compared also locally on the device.

Mobile device's UI, hardware, computational and graphical capabilitiesand limitations are taken into consideration to optimize overallperformance for responsiveness and good user experience and overalloptimization.

Key considerations deals with power management to ensure longer batterylife, memory limitations, CPU bottlenecks and clever Bandwidthmanagement enabling dynamic upload and download of required data to andfrom the mobile device.

Backup Modes

Once positioning accuracies drops under certain threshold a backup modeis initiated. Then the application may ask for current position and aidin navigation by finding the fastest, shortest path to the requireddestination. That is decided automatically based on accuracy and may beeasy for user to feed in as in malls, airports etc. own place is easy toknow from nearby signs, shop names etc.

If internet connection is lost periodically, positioning and navigationis still available in the following manner. Adjacent grid points arepreloaded to the mobile handheld with more points in the currentdirection of movement (and less behind) so localization by comparingcurrent measured multisensory data against nearby points can be doneeven with intermittent internet connection.

In addition a buffer on the device keep recording the sensor data tocreate and re measure grid points. While reconnected to the web, data istransferred from buffer to the server database.

No Additional Hardware Required

The described method and system requires no additional software orhardware for deployment and implementation. Adding hardware in order toenable indoor positioning and indoor navigation is a major disadvantagefor all other indoor positioning and navigation approaches that consistsof additional hardware.

Approaches that requires additional elements (e.g. unique chips to thedevice or special modifications or additional transmitters, routers,WAPs, receivers, sensors etc. to the indoor arena) Even minor changes orinfrastructure installments like Infrared emitters, RFID sensors orPseudolite (for indoor GPS like reception effect) require, when appliedto significant indoor areas, huge expanses on production, installation,integration and maintenance Built-in device chips that will enableindoor navigation may be added in future smartphones. We know thatpenetration rate of new high-end smartphones is slow and so robustindoor positioning solution that is based on additional HW to deviceswill suffer from slow deployment as well.

In addition not all places of interest for public positioning andnavigation are of sufficient commercial value for justifying the relatedeffort of predefined location fingerprinting.

Mandatory Elements

A real indoor navigation system should consist as a minimum onpositioning that enables the above-mentioned accuracy, efficient routingfor navigation (Dijkstra algorithm based or comparable that is based ona graph representation of the simplified map), DB and UI.

User Interface

One key factor is the user experience. A light, 3D/2D interface will befitted. Definitions will control auto adjustment to ambient light forday/night representation. A clear marking of present position withuncertainty circle bleeping overlaid on indoor space map. Destinationwill be available for choosing easily from a database. Display filterswill enable showing places of interest by category (e.g. food, shoes,fashion etc.)

Unique places like restrooms, stairs, escalators, elevators, entrances,exits and emergency exits will be clearly marked. For specialalgorithmic usage for the unique indoor points, please refer to SingularIndoor Points.

Initial Use and Early Adopters Incentive Principals

One challenge is to draw users to download and use the applicationbefore we have sufficient infrastructure (multisensory locationfingerprinting). In that phase places with huge traffic or interest willbe mapped manually. Mapping process is quick and simple using availablesketch and design tools. A store or a room is marked as a node (junctionin a graph) and hallways, exits, entrances (to both indoor areas and tostores) are also marked.

In addition, facilitating the inertial navigation approach, with fixpoints combined with Cellular based navigation will result reasonablepositioning of better than 20 meters indoor even before calibrationstarted. As competition arise and suggest comparable solutions in termsof accuracy (based on better algorithms or improved hardware) thedescribed solution also gets better. Yet the killer application is the‘quiet’ progressive deployment of the crowd sourced multisensory microlocation fingerprinting grid.

Encouraging early adopters to download and install the application mayinvolve mileage kind of benefit. Virtual goods or Pay Pal payments canbe made and badges can be given to those who use it most for mapping theindoor areas of interest for the benefit of future users.

Similar incentives can imply to whoever update a mark a business, markanother point of interest, perhaps as a reaction to a pop up that askshim to do so as deemed needed automatically by the algorithm, do aspecial pattern indoor (e.g. going from the entrance to the nearest exitor nearest unique point such as elevator etc. may boost the griddeployment) or uploads indoor pictures.

Enabling feedbacks on a certain business for the benefit of the close byusers can be done and may be limited to users that are in the proximityof the business while reporting thus limiting unfair commercial bias orspamming.

Other incentives may include sub communities or groups that can sharetips or other information between the group members.

One fundamental advantage is the scalability of the described system andmethod. It can work starting immediately based on current handhelddevices and current infrastructure. The more chips, sensors, basestations, transmission methods etc. that are added to the indoor area,the mobile device or both give advantage to that method as well as forall others.

Yet the underlying idea that lies beneath it gives a major advantage.The more users find it beneficial and adopt it as their indoorpositioning and indoor navigation of choice (leveraging hopefully thefirst mover's advantage) the faster the grid is deployed.

Since grid points accumulated are proprietary it acts like a 2 stagesprogram. Loyal user will see great improvement with no additionalhardware whatsoever. Once regular buildings in peripheral area aresurveyed the grid is built and the positioning accuracies move to thenext level.

There might be a big public interest for example round enabling faster,safer approach for blind people (or otherwise handicapped) to some sortof forgotten government complex in a negligent town. That may drawadditional users.

Classical Use Cases

One preferred mode of action involves launching the application by theuser while driving outdoor, few miles from the indoor area of interest.One very frustrating aspect in working with current outdoor GPS basedapplications is that they take you to the address on time, but then youare left alone looking for parking (underground in many cases) and needmore time to find your way to a specific room or shop (baggage claim,gate etc.).

Such ‘holistic’ approach (namely using as many as possible RF togetherwith device's multisensory data for enabling indoor positioning andnavigation) to positioning can overcome these problems. As indoorparking is mapped (to the extent they can be somehow sampled via themultisensory location fingerprinting described system and method) indoorpositioning and navigation there will be enabled using the system.

A community member (application user) that just leaves his parking (ifhe stopped stepping, and start moving faster than 10 km per hour orconnected the charger for example he is probably driving) may produce anautomated push notification to the one that seeks for parking lot etc.

A user can scan a barcode from a highway advertisement sign and be ledto the mall parking and walked turn-by-turn to the store indoor. He canalso get a notification when he is about to be late for his nextCalendar event based on real time accurate estimation of the time ittakes to walk to his car (busy elevators can be excluded from optimalplanned path and restroom visit may be included) can be consideredtogether with traffic at the underground parking etc.

As generally grid is built (and grid points are added) automaticallyfrom the out inbound, indoor positioning for all users become moreaccurate and hit the required accuracy level of better than 5-7 meters.This level of accuracy is achievable by methods of locationfingerprinting (Wi-Fi and Cell).

Such positioning creates unprecedented indoor navigation experience.Even future accelerometers and gyroscopes alone (pure inertialnavigation) will not enable that level of indoor accuracy unlesshardware is added or periodical calibration is performed. Suchpositioning also record accurate walking paths in real time. Thatfeature is unique and enables, among other uses, valuable analytics onconsumer behavior

Business Model

While surveying the indoor space, application maintains on line andconnected to the web. The connection is standard (Wi-Fi, WAP, Cellularinternet or equivalent) to support collecting the measured grid pointsand actual paths of the devices in the server. This data is transmittedanonymously to avoid privacy issues and possible users concerns.Registered users that downloaded the application (probably in a free orfreemium model—pay per premium features) will enter basic personaldetails upon registration.

One interesting application can display local advertisement in a higherplace in the list, if sponsored. For example, if user is looking for arestaurant, the 2 first results will be the sponsored ones (in hisvicinity) and only later the other by distance from his currentposition. Beside search for businesses, analytics and businessinformation as briefly described, indoor users community is audience tolocation based mobile advertisements. In addition, the proprietaryindoor positioning system (IPS) that is deployed gradually can becommercialized as software development kit (SDK or API or library) forthird parties.

Prospective Applications

Updated accurate 3D maps of indoor places of interest are valuable. 3Dnavigation, augmented reality, find your friends and family, disastercontrol (Panic button that guides you turn-by-turn out of a hotel inemergency), improved Airport time management between leisure, shoppingand getting to the gates, automated parking lot mark and find, optimaltour in a mall, coupons and all other local commercial and localadvertising initiatives as well as pin point accuracy automated check-incheck-out services for foursquare-like applications.

It can also connect to facebook and twitter as a leading indoor micropositioning system and method. As per augmented reality, two differentusers looking on the same shop at the same time can get differentadvertisements, based on their perforations.

Those are just few examples of useful micro-location (common nicknamefor indoor navigation) applications that are based on the staticinfrastructure of a 3D map.

In addition, the traffic in real time, creates an even more excitinglive map in 3D (also known as heat map). Multiple uses include real timeobservation of less and more crowded area, what is going on now, whichlocations are less toured? Why? It might have an impact on striving fordifferential rental rates or trying to improve actual traffic to moreabandoned areas.

Many applications that ease day to day life and somehow help handicappedpersonal relies on planning best route indoor for fitted restrooms,restaurants, avoiding stairs etc.

Another category deals with more optimal time management like when toleave to get to car and out in the highway on time, social elements likefree parking, free coffee or bad experience.

Some basic map editing tools will be available so that indoor businessowners can add their business on the mall map and students can marktheir lab or room on the campus map. Half automated filter will beimplied between absorbed map updates and publishing them to the mapdatabase.

Marking online users (people with devices that are walking or driving)with an icon is supported. Instant messaging between the pedestriancommunity members is enabled. We remain sensitive to privacy issues byomitting the marker from a non-friend active user under 20 meters (orsimilar value) to avoid match of name or nickname with a real strangerin a mall which may arose privacy issues.

Community member are able to share things online, broken ATM, lockedrestrooms, vacant car park or great promotions.

Automated Floor Detection

While surveying in indoor space, as opposed to common outdoor arena,altitude is of great importance. A crucial part of every successfulnavigation algorithm relies on the floor number. Though intuitively itis the easiest part in self orientation (knowing in which floor I am)automatic floor identification is the base for some very coolapplications. Please refer to Application section for examples.

Floors and roofs between different indoor space stories have a distinctnature in term of location fingerprinting map. Wi-Fi signal decreasesdramatically (together with most other WAP reception) while changingfloor.

Together with inertial sensors (Z axis), other inputs like steps sensingor barometric pressure (a sensor that is incorporated in few mobilesmartphones since 2012, while sensitivity and resolution in determiningtypical story change of 3-20 meter per floor is unclear to us at thisstage that can be most probably supported).

Using the GPS signal indoor (although possibly too weak for goodpositioning) is recorded as part of the location fingerprinting datavectors for every single GP but is also used to determine differencesbetween high floors and lower floors in real time. That will beincorporated while sufficient location fingerprinting is collected andanalyzed and by the identification of statistically significantdifferences between basements, mid floors and near roof representativeGPS reception.

Co-Movement (Indoor and Outdoor) of Positioned Users is Analyzed forApplications

Analyzing co-movement of different users may lead location basedpersonal advertisement to another level. For example, 3 moving entities(users) within 15 meters between them that are maybe ‘friends’ (onfacebook or the community that will be established around the herebysuggested system and method) around lunch time can hint on looking forrestaurant with colleagues from work.

Co moving of a user with a user that is his ‘girlfriend’ (by sameprinciples—assuming it's known to the system) on her birthday (known aswell, through profile registration) on evening time may push a couponfor a more romantic place. If it is daytime and user is walking alonethe day before his wife's birthday, present shops and travel agenciesrelated coupons may pop up.

Push message may be used to ask for user help in building the database.Once identified that movement in x, y (corresponds to Northing andEasting for example) is below threshold and no steps are sensed (via theaccelerometer and Dead Reckoning related sensor) while Z (up) isincreasing and GSM signal is down an “Is that an Elevator” message maypop. This should not be done in a pushy or annoying manner and shoulddefinitely be controlled through definitions.

Additional Improvement for Sub-Meter Indoor Positioning Accuracy

Algorithm improvement will eventually include wall, corners and obstacleprediction feature. By analyzing offline the vast collected grid points,sudden changes or inconsistency in Wi-Fi signal strength for adjacentGPs in the location fingerprinting map can point on possible omission ofthe signal and together with described algorithm for floor plan mapcreation can produce better modeling of physical walls, corners, floors,windows etc.

That in turn can lay the foundation for the other positioning approachthat is based on modeling access point position, transmission power,obstacles and implementing a propagation model that can complement thegrid for sub-meter accuracy indoor positioning and indoor navigation.

The described system and method described hereby deals with ‘holistic’approach towards indoor micro positioning. Namely, combining theadvantages of every existing (and future or expected) sensors onboardthe mobile device to enable indoor positioning with no additionalhardware.

The method is not limited for indoor positioning and indoor navigationbut also for every case that involves poor or limited GPS reception(crowded streets, Center Business District, street markets, parking,underground etc.) it may also apply for outdoor if GPS is inoperativedue to poor weather or heavy clouds or GPS system intentional ormalfunctioned shut-down.

Current and near future handheld devices and smartphones can rely oninertial navigation, given (accurate, validated and up-to-date) fixpoints for update every few meters to few tenth of meters. Such updatescan be fused into the device and support reasonable accuracy for longlasting indoor navigation.

A detailed crowd sourced location fingerprinting infrastructure(calibration grid or Radio map), as previously described, that reliesmainly (but not only) on Wi-Fi AP (Access Points) and Cell Towers IDsand signal reception strength per AP may serve as useful fix points.

The mobile device will measure the signal strength from every receivedsource (including but not limited to Bluetooth, WLAN, GSM, GPS, FMRadio, Ultra-Wide Band infrastructures as will be applicable etc.) andperform a real time match between its reading and an array of adjacentpoint readings.

Such a match facilitates known algorithms (such as Kalman Filter orReduced Particles Filter for the inertial navigation together withweighted K nearest neighbors, wKNN, or equivalent) to compute theprobability that the device actual position is on (or close enough) to agrid point. Such methods are used for estimating position based onsuccessive, noisy or fluctuating measurements from Wi-Fi positioning andform inertial navigation sensors.

Once deemed within threshold range a fix is performed, feeding the pointpremeasured absolute coordinates to the total navigation solution. Thusaggregated inertial navigation position drifts are eliminated andpulling the total positioning solution back under threshold. Pleaserefer to system architecture and design considerations.

As described before, the preparation of the Radio map for every place ofinterest indoor (malls, airports, hotels, museums, hospitals, governmentbuildings, high office buildings, parking lots etc.) is a resourceconsuming task. This task is probably in the magnitude of Google Streetsproject in terms of cost and complexity.

It probably requires thousands of employees with high-end measuringdevices and expensive equipment deployed in all points of interestindoors for several years. As mentioned, such location fingerprintingmap is not constant over time, might differ significantly and thusrequires constant updates.

The suggested method and system eases, simplifies and accelerates theresource consuming preparation process of the location fingerprintingmap (also referred to here as Radio map). While facilitated for otherareas, the social power and efficiency of the pedestrians and driversthat carry mobile devices has not yet been harnessed for such task.

Many people that carry their mobile device will contribute to thegradual process of structuring a calibration map. While outdoor, stillwith GPS reception continues fix will be made to the inertial navigationsystem.

When GPS signal is too weak for indoor positioning (yet valuable tospecify each unique indoor place via its recorded locationfingerprinting together with described signals), inertial navigationwill take over and use the time window in which it has acceptableaccuracies. Additional methods, namely dead reckoning (DR), map matchingand Cellular based positioning will elongate this window in a mannerthat will be described.

Both when outdoors and indoors and as long as application is operative,sensor are sampled periodically. Wi-Fi ID and signal strengths, Celltower ID and strength (along with other existing or future built-insensors as deemed such as relevant barometer, ambient air temperatureetc.) such sensors will be recorded every short time interval (probablybetween 3 and ⅕ Hertz) to ensure production of new grid points.

Each collected vector of data is deemed position specific as apedestrian typically moves indoor at a velocity of around 1-2 m persecond and sampling requires around one second. Since the processdescribed here addresses the case in which an indoor place is with noprior data, fix points do not exist.

Fix points are created and collected on a data base on the server. Eachcollected grid point (that will become a fix point) is marked with anumber (grade or rank) that is correlated with its estimated accuracy interms of absolute location and another grade for the quality or lengthof one or more long measurements (made while user was static). Theestimated accuracy is primarily a function of the time since the lastupdate.

Example

The first man to enter an indoor place of interest with his mobiledevice (in pocket, hand, or every other place) for the first time(assuming he had downloaded the application and launched it) will have avery good accuracy upon first losing the GPS signal. Typical accuracy offew meters is achieved by GPS fix and the application inertialnavigation system (with Cellular positioning, DR and map matching)measures his displacement based on the described method.

After one second, we know his absolute position with excellent accuracyand can definitely record a vector of data (Wi-Fi, GSM, GPS/A-GPS andother sensors) for that specific grid point. If we assume that 1 Hz is agood rate (we know his velocity so we can adjust the resolution of therequired grid) he can measure and record 9 additional grid points in thefirst 10 seconds since last fix. AGPS or A-GPS refers to standardassisted GPS on mobile handheld devices.

Please notice that at this stage, in real time, those recorded pointsare useless for him. User can only rely on totally independent methodsthat include Cellular positioning (based on triangulation of cellulartowers), inertial navigation, dead reckoning (DR) and map matching(assuming floor plan was submitted or otherwise added to the systemprior to the first survey which is beneficial but not mandatory).

We can also say that after a period of time, accuracies drops to a pointin which indoor positioning is of limited benefit to him. When more thanone Cellular tower, possibly from more than one cellular operator isavailable, regardless of the one (operator) that is chosen automaticallyfor use (cellular communication), Cellular positioning solutions arealso used by the system and are ranked for their matching in movementdirection and velocity with inertial navigation solution.

However his survey contributed calibrated grid points. He enriched thedatabase with up to date measurements from indoor location. The‘collected’ grid points will in turn serve as fix point for the next (orsame at later time) person (user) entering the same indoor place.

Given an airport for example with few entrances (for simplicity weassume street level parking and not underground although method andsystem is applicable in that case as well). A few early users entered inthe past using the described application on their mobile device(smartphone, handheld etc.).

A critical mass of individuals downloaded, installed and launched theapplication (incentive for doing so before the indoor area is coveredwith measured grid points is described on this paper).

A pedestrian enters the Airport (and loses GPS signal), he is goingthrough an entrance and his device records (automatically) sensor datavectors for new grid points. While walking, after few seconds the deviceidentifies that he is in close vicinity to a (premeasured by earliervisitor) grid point.

The system uses this grid point as a fix point. The inertial device'snavigation system errors are eliminated and he is now ready to penetratedeeper to the Airport hall as it is almost identical to a GPS signalthat fixes his system. He keeps recording (or marking) new grid pointsall the time but their grade (in terms of their position accuracy ishigher again).

After a while many valid grid points are added gradually andautomatically to the database. This enables more fix points for theother users (that runs the application) and gradually support fullpositioning system for the entire indoor Airport hall.

While indoor, the ‘holistic’ (or integrative) approach that we herebydescribe keeps looking for sources for fix points. If for example a GPSsignal will be sensed near a big window that would again increase thegrade (absolute position accuracy wise) of the consecutive (and otheradjacent) collected grid points, while reset all accumulated errorssupporting better positioning right away.

The described method, while requiring no additional hardware (nor tomobile device neither to the indoor area) and eliminates the need in along exhausting, very costly, ongoing calibration process (used tomeasure grid points without harnessing the power of the visitingaudience) maintains, by itself, the infrastructure.

Even well covered indoor areas with crowded, accurate and validated gridpoints (with up to date vector data for each and every point) arerecorded continuously all the time. Thus a missing access point,additional cellular tower or inconsistent Bluetooth reception is easilycoped with.

Since algorithms that are used to compute one's location in real time(for example weighted K nearest neighbors algorithm or similar) relieson a rather wide vector of recorded data. This robust implementation,records an updated data vector for the point (back office handling ofdifferent data vectors for same location will include controlled fusionand publish to database of new data automatically once change isvalidated) The probability analysis yields pretty good guesses as to theexact device position even with omitted, missing, shifted or new Celltowers, access points etc.

The detailed described system and method described herein is new andinnovative as well as implementable for concrete uses in very attractivebusiness areas.

Enhanced Handheld Device's True Movement Direction Extraction.

When trying to measure true movement direction few methods can beimplied. Dead reckoning, pedometer and or inertial navigation are themost common methods.

Inertial navigation uses measured acceleration from built-inaccelerometers. Measured accelerations can be transformed from deviceaxis to Earth coordinates to eliminate the dependency in deviceorientation. Gyroscopes may be used to detect minor changes inorientation etc. Integrating accelerations to changes in velocity andthen integrating again changes in velocity to changes in position canyield the estimated distance in Earth coordinates. When added to a knownstarting point, new estimated position is computed.

The benefits of inertial navigation are that both direction and speedare known. The cons are the rather severe drift in position estimation.When the device is a smartphone, with no additional hardware unit (toimprove sensors), the accelerometers are noisy and may be biased. Bothphenomena can be treated and somehow mitigated. Even when mitigated,double integration of noisy and biased inputs yields a fast drift thatmakes pure inertial navigation with the device unusable after a fewminutes or less.

Pedometer may be complementary to inertial navigation. This approachmakes a few estimations on movement track. Steps can be sensed andstride can be estimated in various ways (human weight and height,outdoor precalibration of step length with available GPS readings—mainlywhen outdoor, strength and pattern of vertical accelerations—towards theEarth center etc.) As for the direction of the movement pedometer israther limited. Map-matching algorithms can be facilitated assuminggiven map or floor-plan or it may require user to hold device straightand level for heading input, thus making the use of the device ridgedand not intuitive.

Dead reckoning on its most primitive implementation assumes knowndirection and velocity or number of steps to estimate new position andmay rely on any combination with pedometer, inertial navigation or both.

The described approach estimates the direction of the device directlyfrom the accelerometers. It eliminates the need in double integration ofnoisy biased built-in device sensors, and makes the use intuitive sinceany device orientation is permitted while walking.

Each Human step can be divided into four distinct segments 201-204 asappears in FIG. 19 that illustrates a user 200. Starting with standing(which may be refer to as segment zero), the first one (marked as 201)is moving the leading leg (right) ahead (in the sketches forward is upfor the top view. For the side view forward is right and right leg ismoved first). The second (202) is laying the leading leg (right) in newposition. The Third (203) is ‘collecting’ our other leg (left) andmoving it forward. The Forth (204) is laying the other leg (left) in thenew position. Then the next step begins etc. For simplicity, we canignore the difference between a first step—from standing (or laststep—to standing) position and between a ‘serial’ step which is not thefirst or last.

Both top view (upper) and side view 201-204 of a standing man. Forwardis towards up for the top view and to the right for the side view. Thereare also provided three lines 201′, 202′ and 203′—located at thestarting point, middle point and end point of the person's step. Theselines are fixed in place and simplify the division of each step to foursegments. They appear for both top views and side views (small redtriangles) as a reference for man movement. On the next pair of images,marked 201 (second for the left) leading leg (right) is moved forward).Later on the next pair of images marked 202, leading leg (right) is puton the ground, setting a new line. On the next pair of images, marked203, the other leg (left) is taken from behind and moved forward and onthe last pair of images (204) the other leg (left) is put on the groundand a third line is added to mark the end of this step. When the leadingleg (right) is taken from behind and moved forward a next step willbegin with segment 201 again

Analyzing the step pattern in terms of accelerations is analyzedschematically in FIG. 20 and reveals that on the first segmentincreasing horizontal accelerations 211 (parallel to Earth surface)towards the leading leg are measured. Those are built in a scatteredmanner that is somehow similar to a fan. The maximal values of theacceleration (denoted 211(1) and 211(2) in FIG. 21) are processed interms of its heading. Averaged Headings are used to determine the mainheading of segment 201.

FIG. 20 illustrates typical acceleration patterns 211-214 as appearswhile walking can be seen here. Segment 201 starts with accelerations211 towards the leading led side (right), then accelerations towardsforward (in this top view, forward is up) gets stronger. Then the bodyis slowing by decelerating and accelerating back (212) towards thecenter segment 202. Symmetrically segment 203 of each step typicallybuild increasing accelerations (213) towards the side of the other leg(left) and forward (up) again while segment 204 decelerates 214 (interms of forward movement) and back to center.

Segment 202 produces a fan like acceleration pattern 212 that indicatesthe body tendency to ‘stop’ just before it launches the other leg(segment 203) and so on. Measured accelerations are changing size anddirection rapidly.

Segment 203, like segment 201 produces a fan like acceleration structure213 towards the other leg, now sent forward. The maximal values of theacceleration (denoted 213 (1) and 213(2) in FIG. 21) are processed interms of its heading. Averaged Headings 221 are used to determine themain heading of segment 203.

Segment 204, like segment 202, produces a fan like acceleration pattern214 that indicates the body tendency to ‘stop’ just before it launchesthe other leg (segment 204) and so on. Measured accelerations arechanging size and direction rapidly.

FIG. 21 illustrates maximal accelerations 211(1) and 211(2) in segment201 and maximal accelerations 213(1) and 213(2) in segment 203. It alsoillustrates the sum vectors 221 and 223 of maximal accelerations insegments 201 and 203. Summing together maximum accelerations (vectors)in segment 201, creates the segment direction 221. Summing up the twosum vectors 221 and 223 for segments 201 and 203 accordingly, revealsthe true estimated step heading 230. In addition, same procedure forsegments 202 and 204 gives us the opposite direction that can furthersupport and fine tune our forward moving direction and help with partialsteps (that do not contain a full step). While filtered over time,actual step direction yield improved solution of actual momentary devicemovement.

As described maximal acceleration vectors per segment are summed.Headings used to determine the main heading of segment 201 and 203. Thenrepresenting vectors for segments 201 and 203 are summed. The sum isused to determine the actual direction of the last step (four segments).That direction put together with any estimation of step size or stride,determines enhanced estimation of difference in position from last knownposition.

Dividing the pedometer to its two primal elements, namely directionestimation and velocity (and or number of steps, and or amount of changein position) and taking care of the direction in the suggested approachenables us to get more out of the device's built-in sensors. We requireno constrains on device orientation and reduce the double integrationrelated errors that come into play when pure inertial navigation isimplemented.

This enhanced pedometer can work stand-alone or as part of indoor (oroutdoor) positioning and navigation system for handheld devices andsmartphones that is based purely on software.

Enhanced Handheld Device's True Orientation Extraction

Device's orientation is concluded based on internal sensors.Accelerometers measure acceleration in device axis. The earthacceleration is approximately 9.81 m/ŝ2 towards the center of Earth andis usually designated 1 g. Since other day-to-day related accelerations(especially walking accelerations) are generally smaller than 1 g,current device's orientation solution performs approximation. Assumingthat the total (vector sum) of all sensed accelerations is also pointingto the center of the Earth simplifies orientation solution. Thissimplified method is a necessity as will be explained.

Having defined the local vertical line pointing towards Earth center inthat manner enables calculating a surface that is parallel to Earth face(and orthogonal to the vertical line) That surface in-turn enablesestimation of the Azimuth or Heading offset from magnetic (or Geometric)North, based on device's built-in magnetometers. Than the elevation canbe measured (Pitch), and tilt can be measured (Roll). Device's built-inGyroscopes readings, when applicable, optionally come into play tosmoothen and better cope with noisy readings.

The device does not ‘know’ it's orientation and we cannot assume thedevice is held straight and level when operating. Estimated Heading,Pitch and Roll can be used (using rotation matrix or equivalent methods)to transform device measured data (accelerations, magnetic field etc.)to Earth coordinate system.

Accelerations are measured in 3 device's axis. The left picture of FIG.22 depicts typical measurement of 3D accelerometer. Such measurement indevice axis is marked 90. Total of three measurements correspond todevice axis x, y and z accordingly. Assuming the device is static (weassume its location and orientation are fixed although in general caseit can rotate around itself but is latitude, longitude and altitude arefixed) the total acceleration is pointing to the center of Earth, marked100. Based on that calculation an orthogonal surface 110 can be found.As 110 is perpendicular to 100, it's assumed parallel to theoreticalEarth surface. This surface 110 is the reference for the device'sorientation computations. Magnetic North 120 as measured by theMagnetometer and is part of 110 (the surface). Device Heading 150 is theangular difference between the projection 130 of the device'slongitudinal axes 140 on 110 (the surface) and between 120 (the MagneticNorth). Pitch 160 (the angle between 130 and 140) and Roll 180 (theangle between latitudinal devices axis 170 and between 190 when 190 isorthogonal to 130 and part of 110) are computed accordingly (inreference to 110 AND 120) and a full set of three angles represents thedevice orientation. The dashed arrows that appear on the center andright drawing (FIG. 22) represent 130—the projection of longitudinaldevice axes on surface 110 and 190—the projection of latitudinal deviceaxes on surface 110. 130 and 190 are orthogonal and are both belong tosurface 110. Only when Pitch is Zero 140 is part of 110. Then if Roll isZero 170 is part of 110. When heading is Zero 120 and 130 arecoinciding.

A built-in loop is created since the transformation of deviceaccelerations to Earth (simplified) coordinates is required forestimating device orientation, which in-turn is used to convert deviceaccelerations (and other vector data) to Earth coordinates creates aninherent circle. That explains the necessity for the above mentionedsimplified method (that assumes that total vector sum of all measuredaccelerations points to the center of Earth).

As long as horizontal accelerations (parallel to Earth face) arenegligible, the error in vertical line 100 estimation (as well as forcorresponding errors in Heading 150, Pitch 160 and Roll 180) isinsignificant. However, when high precision is required (e.g.implementing inertial navigation), those relatively minor errors indevice orientation pose serious challenges (especially when doubleintegration—the mathematical procedure associated with extracting changein velocity based on measured accelerations, and changes in positionbased on velocity computed changes—is involved. When accelerations thatare related to walking get into play, up to 1-2 g's can be measured inthe movement direction and significantly bias the direction of the line100 that should point down. These errors ‘tilt’ the theoretical surface110, causing bigger and bigger device orientation errors. Those errorscause bigger mistakes in next time unit computation of device's heading150, pitch 160 and roll 180.

FIG. 23 illustrate that additional walking acceleration 105 is added,marked in Black arrow. For simplicity we assume that orientation isfixed and identical to FIG. 22 and very little time passed. There is away to know we started walking because we were static (i.e. no stepswere sensed if we use a pedometer or otherwise) and now we are notstatic. The walking related acceleration 105 is added to the former Z100 arrow, and the computed Z 100′ is now erroneous since it is nolonger pointing to the Earth center. Accordingly we see that the surface110′, marked in Grey, is tilted to the left and pitched a little upbecause the surface, by definition, is orthogonal to 100′. The outcomeon the right side is considerably different pitch and roll angles.

The surface 110′ pitches up (that is not real but an outcome of addingacceleration 105) ‘reduces’ the computed pitch as the baseline forcomputation surface 110′ got closer to the device's longitudinal axis(FIG. 22140) that was computed in earlier cycle (right side of FIG. 22).The surface 110′ left tilt increases and so computed right roll assurface 110′ got further away from the reference (FIG. 22190) that wascomputed in earlier cycle. As a result the device now computes roll 180′and pitch 160′ that are different then the case when device was static(user did not walk). Similarly, errors in heading 150′ occur whensurface 110′ replaces previous surface 110. Although no actual change inorientation occurred (assumed for simplicity) the addition ofacceleration 105 yield erroneous orientation computations that in turncreate erroneous breakdown of device accelerations and conversion toEarth coordinate system. It happens because we started to walk and soacceleration 105 was added but we continued estimating the vector thatpoints to the center of Earth 100′ (previously 100) and based on thatupdated surface 110 to 110′. Those mistaken values are the input for thenext time unit etc. The device on FIG. 23 to the right appears withsmaller up pitch and bigger right roll then the one in FIG. 22 to theright. Errors in the device's magnetic heading can occur as well. Thedashed arrows that appear on the center and right drawing (FIG. 23)represent 130′—the projection of longitudinal device axes on surface110′ and 190′—the projection of latitudinal device axes on surface 110′.130′ and 190′ are orthogonal and are both belong to surface 110′.

The system and method described hereinafter takes advantage of the factthat movement tracking may assume some assumptions on Human movement. Ifwe identify a walking pattern, for example by repeating significantchanges in accelerations, by pedometer or by position changes etc. wecan estimate the size and direction of horizontal accelerations. We canalso assume that Earth surface is locally flat and level (that is evenmore accurate for indoor positioning).

In a similar manner, when we are not walking (we identify that by apedometer that indicates no steps for example) we can sometime assumethat we are not moving at all (not always, because we may be moving onan escalator, or even a vehicle while not walking) In that case we knowthat the total acceleration 100 should point to the Earth center andthus the orthogonal surface 110 (parallel to Earth surface) is the mostaccurate one. We set this a baseline and when we identify steps (startwalking), we expect an error in the vertical vector direction 100′computation (because of the added horizontal accelerations 105 ofwalking) We then compensate for that by not taking into considerationthose horizontal accelerations 105 (that are directly related to walkingnow) and stick to the precomputed orientation reference 110 (orthogonalsurface—parallel to Earth surface) instead.

Once we have estimated the size and direction of horizontalaccelerations 105 we can leave it out when computing the local verticalline 100 (that points to the Earth center). Please refer to FIG. 23 asif we eliminated the Black arrow 105 since we know it's related towalking to estimate if a ‘mixture’ of horizontal accelerations withvertical 100 (Earth related acceleration) occurred, the totalacceleration is also considered. In case that total acceleration issignificantly different then 1 g we suspect that such mixture occurredand eliminate the horizontal vector from the momentary vertical linedirection and size computation. The horizontal walking relatedacceleration cannot be measured purely but is rather included in thedevice 3D acceleration measurement, yet we suspect it's there and do nottake a relevant portion of horizontal accelerations into account. Theoutcome is closer to FIG. 22—we walk, but we know that we walk, so wekeep computing the line towards Earth center 100 based entirely (aspossible) on Earth related accelerations, thus minimizing walk relatedhorizontal accelerations effect on orientation errors because wemaintain 110 (and not 110′) as our reference surface mitigating errorsin orientation.

Device's built-in Gyroscopes readings are also used to evaluate theactual change in orientation from previous time measures. Actual (true)device orientation change can be also sensed as changes in Gyroscopereadings that represent angular accelerations. That is important becausedevice's orientation changes can be concluded also from changes (such asnoisy or biased accelerometer reading or changes in magnetic readings)that are not necessarily related to actual orientation changes.

Such manipulation enables more accurate estimation of the local verticalline 100. That in turn enables better estimation of the surface 110 thatis parallel to Earth face, and in-turn reduces errors in Azimuth 150,Pitch 160 and Roll 180. Reduced errors in device's orientationsignificantly enhance double integration accuracy and enable improvedinertial navigation and other applications.

Integrating commercial sensors (accelerometers, gyroscopes,magnetometers etc.) into smartphones creates many opportunities forlocation related applications. Inertial navigation is one exciting use.Sensors in the device are, although improving, noisy and biased.Inertial navigation is extremely susceptible to noisy and biased sensorsdata due to its computational nature. Reducing errors in device's sensedorientation is a key for better estimation of related physical sizes.

That is achieved by examining the accelerations, gyroscopes andmagnetometers readings in a more comprehensive way. The accelerations,when applicable, are divided into vertical (Earth related) andhorizontal (movement related). Vertical line 100 (that points to theEarth center) is more accurately estimated.

The theoretical surface 110 that is parallel to Earth face (andorthogonal to the vertical line 100) is more accurate in terms of itsPitch 160 and Roll 180. Splitting next cycle's readings is more preciseand so 110′ is closer in orientation to 110 (assuming for simplicitythat no actual orientation change occurred). Double integration (as wellas other application) drifts slower and produces better estimations.

Enhanced Indoor Navigation Positioning Accuracy

Few methods are considered for indoor navigation. When GPS reception isinsufficient, other approaches are considered for accurate positioning.Those methods may include location fingerprinting. That is done througha process of recording location specific readings in terms of RadioFrequencies (RF) such as Wi-Fi Access Points (WAPs), GSM, FM, EarthMagnetic field, ambient sound, ambient light and additional sensibledata based on device's capabilities. Those can be combined with cellularlocation (that is mostly available indoors though not very accurate) toachieve better indoor positioning accuracies.

Such location finger printings may differ, among other things, by theirnature, physical attributes, scale, variations, metadata, resolution andthus achievable accuracy. Better understanding of the overall andcurrent accuracy of each method enables enhanced indoor positioning.

A system and method for improving indoor positioning (that can be fullyapplicable to outdoor as well, to improve, complement or replace GPSreception) is hereby disclosed.

State of the art cellular positioning (such as the one Google implementsin Google Maps mobile application) usually produces no better than 30 maccuracy. Magnetic field measurement that is compared to a learning set(that was collected in advance) may sometimes distinct between pointsthat 10 cm apart. Location fingerprinting based Wi-Fi positioning(collected by precalibration that is also known as war driving or warwalking) can easily detect different floors and get in floor accuracy of4-7 m. FM location finger printing may enable accuracy of ˜10 m indoors.

The down side with the previously described very high resolution ofmagnetic field is that its units are micro Tesla and every place onearth corresponds with values well within that only few (+−) hundreds ofmicro Tesla. As for the magnetic field direction, same problem occurs asit is expressed in degrees (0-359). It may tell the difference betweentwo adjacent points indoors, but all over the world, numerous pointshave same or very similar readings. Similarly, FM radio signals maydistinct between two nearby points indoor as their locationfingerprinting are different but may as well be identical to many otherpoints over huge area.

When we try to harness magnetic field data for indoor navigation,starting with comparison of real time magnetic field data against theprecollected learning set may prove useless. The algorithm may find itextremely similar to too many different points. If however we know thatwe are in an uncertainty area of few meters by few meters, magneticfield may prove the best tool to refine our accuracy further. Similarly,if we know that we are within few 10 s of meters, FM Radio can help usrefine the accuracy.

The funnel-like approach expands this method to all currently available(and future detectable readings, such as emerging cellular pico-cells orfemto-cells and enterprise level APs as well as digital video broadcast(DVB) etc.) location finger printing sources. The method dynamicallydefines the order from coarser towards finer and finer positioning aids.Dynamic implementation of location fingerprinting related (and other)signals by their increased real time accuracy may be described as afunnel narrowing down. If we know that the cellular positioning enables30 m accuracy now, we then apply the FM (or another source as may beavailable in real time) for reducing the uncertainty. That in-turnimproves the accuracy ˜10 m (which is for example the current FMaccuracy indoor). Only then the WAPs (Wi-Fi Access Points) may come intoplay and improve the indoor positioning accuracy to 5 m. Then themagnetic field is used to choose the in-room positioning and get to 1-2m accuracy.

That described system and method constantly facilitates statisticalalgorithms to evaluate the accuracy (or uncertainty in localization) ofevery different positioning module. It then marks it automatically witha corresponding dynamic weight that represents its current accuracy andlevel of certainty. If for example no WAPs are sensed (lower levelparking for example), that may change the order of the chain to relymore on other reliable sources. If the FM accuracy currently dropped to40 m and for some reason the cellular positioning inaccuracy is now 30 mcell location will be used to refine FM based positioning (or we zeroesFM weight and so ignore it temporally—until its accuracy is betteragain). In that case, map matching algorithm will be used tosignificantly enhance the accuracy.

It is important to maintain the funnel-like shape—a hierarchy from loweraccuracy towards higher accuracy dynamically, in real time, based ontrue available data because using ridged predefined (as oppose to realtime dynamic) order would use map matching, for example, at a certainstage regardless of the available sources or may try to match magneticfield readings as first step resulting many possible matches and thussuboptimal accuracy.

Another aspect of using only the available positioning modules at thecorrect order is improved efficiency as cleverer dynamic downloading(from the server to the device) of only relevant database subsets,perhaps with some margins, is enabled. When funnel-like accuracyenhancement is implemented less data points that serves as base line forcomparison are transmitted back and forth. That in-turn requires muchless data traffic, less load, smaller band width and significantlylonger device battery life.

FIG. 24 illustrates a method 280 according to an embodiment of theinvention.

The method 280 may be used for building a multisensory location map, andmay include: (I) receiving (282), by an interface or automatically,multiple multisensory data vectors acquired by multiple mobile devicesat multiple locations and location estimates indicative of the multiplelocations; wherein at least a majority of the multiple locations arelocated within an area in which an accuracy of global positioning system(GPS) based navigation is below an allowable threshold; wherein thelocation estimates are at least partially generated by internalnavigation systems of the multiple mobile devices; and calculating(284), by a map calculator, in response to the multiple multisensorydata vectors and the location estimates, a location fingerprinting mapthat comprises multiple grid points, wherein each grid point comprises amultisensory grid point fingerprint and grid point location information.

The calculating (284) may include correlating, by the map calculator,the multiple multisensory data vectors and the location estimates, toprovide the location fingerprinting map.

The method may include repetitively feeding (286) to a mobile devicethat enters the area, location information for fixing location estimategenerated by the internal navigation system of the mobile device.

The method may include updating (288) an accuracy level of grid pointlocation information. The updating can be made in response to a numberof mobile devices that acquired multisensory data vectors in proximityto the grid point, in response to an acquisition of location specificelectromagnetic signals acquired at a vicinity of the grid point, inresponse to global positioning system (GPS) signals acquired, even ifinsufficient for positioning solution, in proximity to the grid point orin combination thereof.

The method may include detecting (290) indoor space singularity pointsand updating an accuracy level of grid points located in proximity tothe singularity points.

The method may include building (292) a map of the indoor space basedupon the content of the location fingerprinting map.

The method may include sending (294) to mobile device directionalinformation in response to a location of the mobile device, a targetdestination and the location fingerprinting map.

The method can include calculating a location fingerprinting map that isa three-dimensional map.

The method may include receiving (296) multiple multisensory datavectors acquired by a mobile device at a plurality of indoor locationsbefore receiving a multisensory data vector at a certain location inwhich the granularity of the GPS based navigation is below thethreshold, and increasing an accuracy level of the location estimates ofthe plurality of indoor locations based upon GPS location informationacquired at the certain location.

The method may include receiving (298) multiple multisensory datavectors acquired by a mobile device at a plurality of indoor locationsafter receiving a multisensory data vector at a certain location inwhich the granularity of the GPS based navigation is below thethreshold, and increasing an accuracy level of the location estimates ofthe plurality of indoor locations based upon GPS location informationacquired at the certain location.

The multisensory data vector may include electromagnetic information,including communication related electromagnetic signals (signalsgenerated during communication), at least one out of ambient noise,temperature, earth magnetic field information and even pedometerinformation.

Especially, stages 288, 290, 296 and 298 may be included in stage 284and stages 286, 292 and 294 may follow stage 284.

FIG. 25 illustrates a method 300 according to an embodiment of theinvention.

Method 300 may include: receiving (310) location information of acertain location in which a mobile device is located, the locationinformation is acquired by utilizing global positioning system (GPS)based navigation; receiving (312) multiple multisensory data vectorsacquired by the mobile device at multiple locations and locationestimates indicative of the multiple locations; wherein the locationestimates are generated by an internal navigation system of the mobiledevice; and associating (314) an accuracy level to the locationestimates based upon an estimated accuracy of the internal navigationsystem and a distance of each location from the certain location.

The accuracy levels may decrease with an increase of a distance orelapsed time from the certain point.

Method 300 may also include receiving (320) location information ofanother location in which the mobile device is located, the locationinformation is acquired by utilizing GPS based navigation; wherein thehand-held device reaches the other location after reaching the multiplelocations; and updating (322) the accuracy level of the locationestimates of the multiple points based upon the estimated accuracy ofthe internal navigation system and a distance of each location from theanother location.

The method may further include receiving (324) multiple multisensorydata vectors and location estimates from another mobile device andupdating (326) an accuracy level to location estimates generated by theother mobile device based upon the location information acquired by themobile

FIG. 26 illustrates a method 400 according to an embodiment of theinvention.

Method 400 may include: acquiring (412) by a mobile device globalpositioning system (GPS) location information; acquiring (414), afterstopping acquiring GPS location information, a multisensory data vectorand generating (416), by internal navigation system, a location estimateof a location in which the multisensory data vector was acquired;comparing (420) the acquired multisensory data vector to at least onemultisensory data vector of at least one grid point of a locationfingerprinting map and determining a location of the mobile device basedupon the comparison; and updating (428) the internal navigation systemwith the location while eliminating the internal navigation systemaccumulated drift.

The multisensory data vector comprises earth magnetic field readings andcommunication related electromagnetic signals; wherein the determining(420) of the location may include calculating (425) a location estimateof the mobile device based upon a comparison between communicationrelated electromagnetic signals acquired by the mobile device andcommunication related electromagnetic signals of the at least one gridpoint of the location fingerprinting map; and updating (426) thelocation estimate based upon a comparison between each magnetic fieldreadings acquired by the mobile device and magnetic field readings ofthe at least one grid point of the location fingerprinting map.

FIG. 27 illustrates a method 500 according to an embodiment of theinvention.

Method 500 may include: receiving (510), by an interface orautomatically, multiple multisensory data vectors acquired by multiplemobile devices at multiple locations and location estimates indicativeof the multiple locations; wherein at least a majority of the multiplelocations are located within an area in which an accuracy of globalpositioning system (GPS) based navigation is below an allowablethreshold; wherein the location estimates are at least partiallyobtained by processing accelerometers readings; and calculating (520),by a map calculator, in response to the multiple multisensory datavectors and the location estimates, a location fingerprinting map thatcomprises multiple grid points, wherein each grid point comprises amultisensory grid point fingerprint and grid point location information.

FIG. 28 illustrates a method 600 according to an embodiment of theinvention.

Method 600 may include: receiving (610), by an interface orautomatically, multiple multisensory data vectors acquired by multiplemobile devices at multiple locations and accelerometer readings obtainedwhen a user moved between the multiple locations; wherein at least amajority of the multiple locations are located within an area in whichan accuracy of global positioning system (GPS) based navigation is belowan allowable threshold; estimating (620) the position of the multiplelocations by processing the accelerometers readings; and calculating(630), by a map calculator, in response to the multiple multisensorydata vectors and the location estimates, a location fingerprinting mapthat comprises multiple grid points, wherein each grid point comprises amultisensory grid point fingerprint and grid point location information.

The estimating (620) may include extracting, out of accelerometerreadings, information relating to multiple walking phases; andprocessing accelerometer information relating to two walking phases todetermine a direction of propagation of a user.

The estimating (620) may include detecting maximal accelerometerreadings related to first and third walking phases out of four walkingphases; and determining the direction of propagation of the user inresponse to the maximal accelerometer readings.

The estimating (620) may include detecting maximal accelerometerreadings related to second and fourth walking phases and estimating thedirection of propagation of the user in response to the maximalaccelerometer readings related to each one of the first till fourthwalking phases.

FIG. 29 illustrates a system 700 according to an embodiment of theinvention.

System 700 includes a computer 710 that can receive information frommultiple mobile devices 720. The computer 710 can be a server or anyother type of computer. The computer can include a communicationinterface 712 and a map calculator 714. The mobile devices 720 may havemultiple sensors 721 and 722. The number of sensors and their types mayexceed two. For simplicity of explanation only one of the mobile devicesis shown in the figure as including multiple sensors 721 and 722.

System 700 can execute any of the mentioned above methods. For example,computer 710 can execute any of these methods mentioned above while themobile devices 720 may execute method 400. The mobile devices 720 mayhost an application that receives the map.

The present invention discloses, inter alia, a computerized system forbuilding a multisensory location map, the system comprising an interfacefor receiving multiple multisensory data vectors acquired by multiplemobile devices at multiple locations and accelerometer readings obtainedupon movement of at least one device carried by at least one userbetween the multiple locations; at least a portion of said movementbeing walking; at least a majority of the multiple locations are locatedwithin an area in which an accuracy of global positioning system (GPS)based navigation is below an allowable threshold; and a processor,interconnected with said interface, with accelerometers, a magnetometerand a map calculator, configured for: extracting, out of accelerometerreadings, accelerometer information related to multiple walking phasesof the walking; for at least two of said multiple walking phases,real-time correcting a currently measured Z vector, and a pitch angleand a roll angle thereof, thereby compensating for horizontalaccelerations, thereby obtaining a Z vector pointing toward Earth'scenter; calculating, from said Z vector pointing toward earth's center,a surface parallel to Earth's face (perpendicular to said Z vectorpointing toward earth's center); estimating, from said surface parallelto Earth's face and from a magnetic north measured by at least onebuilt-in magnetometer in said at least one device, an offset selectedfrom a group consisting of: an azimuth offset from magnetic north and aheading offset from geometric north; processing the accelerometerinformation related to said at least two of said multiple walking phasesto determine a direction of propagation of the at least one user andcorrecting said direction of propagation based on said offset; andestimating, from said corrected direction of propagation, at least onelocation of said at least one user; and the map calculator calculating,in response to the multiple multisensory data vectors and said at leastone estimated location, a location fingerprinting map that comprisesmultiple grid points; each of the multiple grid points comprising amultisensory grid point fingerprint and grid point location informationderivable from said at least one estimated location.

The invention may also be implemented in a computer program for runningon a computer system, at least including code portions for performingsteps of a method according to the invention when run on a programmableapparatus, such as a computer system or enabling a programmableapparatus to perform functions of a device or system according to theinvention.

A computer program is a list of instructions such as a particularapplication program and/or an operating system. The computer program mayfor instance include one or more of: a subroutine, a function, aprocedure, an object method, an object implementation, an executableapplication, an applet, a servlet, a source code, an object code, ashared library/dynamic load library and/or other sequence ofinstructions designed for execution on a computer system.

The computer program may be stored internally on a non-transitorycomputer readable medium. All or some of the computer program may beprovided on computer readable media permanently, removably or remotelycoupled to an information processing system. The computer readable mediamay include, for example and without limitation, any number of thefollowing: magnetic storage media including disk and tape storage media;optical storage media such as compact disk media (e.g., CD-ROM, CD-R,etc.) and digital video disk storage media; nonvolatile memory storagemedia including semiconductor-based memory units such as FLASH memory,EEPROM, EPROM, ROM; ferromagnetic digital memories; MRAM; volatilestorage media including registers, buffers or caches, main memory, RAM,etc.

A computer process typically includes an executing (running) program orportion of a program, current program values and state information, andthe resources used by the operating system to manage the execution ofthe process. An operating system (OS) is the software that manages thesharing of the resources of a computer and provides programmers with aninterface used to access those resources. An operating system processessystem data and user input, and responds by allocating and managingtasks and internal system resources as a service to users and programsof the system.

The computer system may for instance include at least one processingunit, associated memory and a number of input/output (I/O) devices. Whenexecuting the computer program, the computer system processesinformation according to the computer program and produces resultantoutput information via I/O devices.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

The connections as discussed herein may be any type of connectionsuitable to transfer signals from or to the respective nodes, units ordevices, for example via intermediate devices. Accordingly, unlessimplied or stated otherwise, the connections may for example be directconnections or indirect connections. The connections may be illustratedor described in reference to a single connection, a plurality ofconnections, unidirectional connections, or bidirectional connections.However, different embodiments may vary the implementation of theconnections. For example, separate unidirectional connections may beused rather than bidirectional connections and vice versa. Also,plurality of connections may be replaced with a single connection thattransfers multiple signals serially or in a time multiplexed manner.Likewise, single connections carrying multiple signals may be separatedout into various different connections carrying subsets of thesesignals. Therefore, many options exist for transferring signals.

Although specific conductivity types or polarity of potentials have beendescribed in the examples, it will be appreciated that conductivitytypes and polarities of potentials may be reversed.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturescan be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Also for example, the examples, or portions thereof, may implemented assoft or code representations of physical circuitry or of logicalrepresentations convertible into physical circuitry, such as in ahardware description language of any appropriate type.

Also, the invention is not limited to physical devices or unitsimplemented in non-programmable hardware but can also be applied inprogrammable devices or units able to perform the desired devicefunctions by operating in accordance with suitable program code, such asmainframes, minicomputers, servers, workstations, personal computers,notepads, personal digital assistants, electronic games, automotive andother embedded systems, cell phones and various other wireless devices,commonly denoted in this application as ‘computer systems’.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

The invention claimed is:
 1. A computerized method for building amultisensory location map, the method comprising: receiving, by aninterface, multiple multisensory data vectors acquired by multiplemobile devices at multiple locations and accelerometer readings obtainedupon movement of at least one device carried by at least one userbetween the multiple locations; at least a portion of said movementbeing walking; at least a majority of the multiple locations are locatedwithin an area in which an accuracy of global positioning system (GPS)based navigation is below an allowable threshold; extracting, out ofaccelerometer readings, accelerometer information related to multiplewalking phases of the walking; for at least two of said multiple walkingphases, by means of said accelerometer information, real-time correctinga currently measured Z vector, and a pitch angle and a roll anglethereof, thereby compensating for horizontal accelerations, therebyobtaining a Z vector pointing toward Earth's center; calculating, fromsaid Z vector pointing toward earth's center, a surface parallel toEarth's face (perpendicular to said Z vector pointing toward earth'scenter); estimating, from said surface parallel to Earth's face and froma magnetic north measured by at least one built-in magnetometer in saidat least one device, an offset selected from a group consisting of: anazimuth offset from magnetic north and a heading offset from geometricnorth; processing the accelerometer information related to said at leasttwo of said multiple walking phases to determine a direction ofpropagation of the at least one user and correcting said direction ofpropagation based on said offset; and estimating, from said correcteddirection of propagation, at least one location of said at least oneuser; and calculating, by a map calculator, in response to the multiplemultisensory data vectors and said at least one estimated location, alocation fingerprinting map that comprises multiple grid points; each ofthe multiple grid points comprising a multisensory grid pointfingerprint and grid point location information derivable from said atleast one estimated location; wherein at least one of the following isbeing held true: said method further comprising step of sendinginstructions to at least one of said multiple mobile devices directionalinformation in response to a location of said at least one of saidmultiple mobile devices, a target destination and said locationfingerprinting map; or said method further comprising steps ofacquiring, by an internal navigation system of at least one of saidmultiple mobile devices, a second multisensory grid point fingerprint;estimating by said internal navigation system, at least one navigationsystem location of said at least one user; comparing said secondmultisensory grid point fingerprint to at least one of said firstmultisensory grid point fingerprint; and estimating a location of saidat least one of said multiple mobile devices from said comparison. 2.The computerized method according to claim 1, further comprising step ofprocessing location information indicative of paths of multiple userswithin the indoor area to provide path information indicative of pathsof the multiple users; and processing the path information to generatean estimated three-dimensional map of the indoor area.
 3. Thecomputerized method according to claim 1, wherein at least one of themultiple multisensory data vectors comprises at least one of: ambientnoise, temperature, and earth magnetic field information.
 4. Thecomputerized method according to claim 1, wherein at least one of themultiple multisensory data vectors comprises pedometer information. 5.The computerized method according to claim 1, further comprising a stepof selecting said order of use to be from less accurate grid pointlocation information to more accurate grid point location information.6. A computerized system for building a multisensory location map, thesystem comprising: an interface for receiving multiple multisensory datavectors acquired by multiple mobile devices at multiple locations andaccelerometer readings obtained upon movement of at least one devicecarried by at least one user between the multiple locations; at least aportion of said movement being walking; at least a majority of themultiple locations are located within an area in which an accuracy ofglobal positioning system (GPS) based navigation is below an allowablethreshold; a processor, interconnected with said interface, withaccelerometers, a magnetometer and a map calculator, configured forextracting, out of accelerometers readings, accelerometer informationrelated to multiple walking phases of the walking; for at least two ofsaid multiple walking phases, by means of said accelerometerinformation, real-time correcting a currently measured Z vector, and apitch angle and a roll angle thereof, thereby compensating forhorizontal accelerations, thereby obtaining a Z vector pointing towardearth's center; for calculating, from said Z vector pointing towardEarth's center, a surface parallel to Earth's face (perpendicular tosaid Z vector pointing toward Earth's center); for estimating, from saidsurface parallel to Earth's face and from a magnetic north measured byat least one built-in magnetometer in said at least one device, anoffset selected from a group consisting of: an azimuth offset frommagnetic north and a heading offset from geometric north; for processingthe accelerometer information related to said at least two of saidmultiple walking phases to determine a direction of propagation of theat least one user and correcting said direction of propagation based onsaid offset; for estimating, from said corrected direction ofpropagation, at least one location of said at least one user; and saidmap calculator calculating, in response to the multiple multisensorydata vectors and said at least one estimated location, a locationfingerprinting map that comprises multiple grid points, each of themultiple grid point comprising a multisensory grid point fingerprint andgrid point location information derivable from said at least oneestimated location wherein at least one of the following is being heldtrue: an order of use of said multiple multisensory data vectors isdynamically real-time variable, said order of use being a function ofreal-time accuracy of said grid point location information; or saidprocessor is additionally configured for: acquiring, by an internalnavigation system of at least one of said multiple mobile devices, asecond multisensory grid point fingerprint; estimating by said internalnavigation system, at least one navigation system location of said atleast one user; comparing said second multisensory grid pointfingerprint to at least one of said first multisensory grid pointfingerprint; estimating a location of said at least one of said multiplemobile devices from said comparison; and updating said internalnavigation system with said location estimated from said comparison. 7.The computerized system according to claim 6, said processor isadditionally configured for processing location information indicativeof paths of multiple users within the indoor area to provide pathinformation indicative of paths of the multiple users; and processingthe path information to generate an estimated three-dimensional map ofthe indoor area.
 8. The computerized method according to claim 6,wherein at least one of the multiple multisensory data vectors comprisesat least one of members of a group consisting of ambient noise,temperature, and earth magnetic field information.
 9. The computerizedsystem according to claim 6, wherein at least one of the multiplemultisensory data vectors comprises pedometer information.
 10. Thecomputerized system according to claim 6, wherein said order of use isselected to be from less accurate grid point location information tomore accurate grid point location information.